Semantic information processing

ABSTRACT

A system for exchanging various forms of information between computer-executable agents. A computing device is configured to determine semantic data associated with each data object (DO) of a plurality of DOs. Each DO is associated with a location, and the semantic data describes the content of the associated DO. The computing device receives, from a first user computing device, a request for DO information and, in response to the request, provides DO information including the locations and the semantic data associated with the retrieved DOs to the user computing device by (a) transmitting the locations and the semantic data to the first user computing device, and/or (b) instructing the first user computing device to request the DO information from a second user computing device to which the locations and the semantic data were previously transmitted.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional Application No.61/426,951, filed 23 Dec. 2010, which is hereby incorporated byreference in its entirety.

BACKGROUND

Aspects of the disclosure relate to information exchange andtransmission, the semantic internet, and intelligent agents. Inparticular, aspects of the disclosure relate to a new and useful systemthat efficiently allows intelligent agents to communicate, interact,consume and process semantic data, and present useful information tousers for a personalized and secure internet experience.

Semantics is the study of meaning. The semantic web is a termencompassing a vision of the internet in which machines, such as agents,are semantically intelligent and understand the meaning of theinformation they consume.

The vision of a semantic internet has yet to be realized as web pagesare meant for humans to read and not machines. To get around thisproblem, semantic schemas and languages have been developed so thatmachines may better understand text encountered on the internet. Some ofthe popular schemas include XML, RDF, OWL, and SPARQL. Usually a personmanually transforms a human readable web page, usually in HTML, into acorresponding machine readable page in one or more of these formats orprotocols.

SUMMARY

Briefly stated, aspects of the disclosure involve a system in whichvarious forms of information, such as semantic information andintelligence about data objects, can be efficiently transferred betweenautonomous and semi-autonomous intelligent agent programs. The systemuses P2P and/or centralized transmission schemas to effectively sharedata and limit network and computer resource usage such as bandwidth andprocessor power. The system includes three parts. It contains onecentral point referred to as a master agent, transmission schemas forinformation exchange, and a network of connected user agents. Thetransmission schemas allow user agents and a centralized master agent toexchange information with each other. This network architecture providessolutions for connecting producers and consumers directly togetherthereby increasing the dissemination of pertinent information to users.It provides security and privacy, allowing for the sharing of networkingresponsibilities, and increasing the marketability of the internetitself.

Further, aspects of the disclosure involve a system in which aself-organizing intelligence construct can efficiently and accuratelymake information decisions on behalf of its user. It can predict whichDataObjects the user would be interested in consuming over the domain ofinternet DataObjects. As a user's preferences change over time so doesthe adapting intelligence construct. The construct can also communicatewith other constructs throughout the internet in both a centralized ordecentralized manner. The system can keep user consumption informationprivate from central points like websites while still being able toserve up personalized, targeted ads and DataObjects to the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an information exchange system with aMaster Agent (SIE, CIE, DB, WS), transmission lines, and user agentswith diagram information propagation sequence.

FIGS. 2A and 2B are a flowchart of an exemplary method of master agentcommunicating with a user agent for getting new DataObjects.

FIG. 3 is a diagram of network raw propagation sequence.

FIG. 4 is a flowchart of an exemplary method of centralized DataObjectinformation propagation.

FIG. 5 is a flowchart of an exemplary method of master agent retrievingor receiving DataObjects, evaluating them, compressing them,distributing them, and agents receiving them.

FIG. 6 is a diagram of an exemplary centralized model of informationpropagation.

FIG. 7 is a flowchart of an exemplary method of decentralized DataObjectinformation propagation.

FIG. 8 is a diagram of an exemplary decentralized model of informationpropagation.

FIG. 9 is a flowchart of an exemplary method of a master agent operatingas a certifying authority for secure communication.

FIG. 10 is a diagram of Public/Private Key Exchange.

FIG. 11 is a diagram of anonymization network of agents concealingprivate information like ads the user consumes and validation ofreporting with master agent.

FIG. 12 is a flowchart of an exemplary method of anonymizationconcealment between agents and validation with master agent.

FIG. 13 is a diagram of exemplary model of Anonymous ConsumptionReporting Error Resolution.

FIG. 14 is a diagram of exemplary model for connecting producers andconsumers of information.

FIG. 15 is a flowchart of an exemplary method of collective dataaggregation on master agent, processing the collective data, andredistribution to other agents.

FIG. 16 is a diagram of an anonymized DataObject request to a websiteusing another agent.

FIG. 17 is a block diagram of an information system including an agentsystem.

FIGS. 18A and 18B are a flowchart of an exemplary method of agent takingin information, ranking DO, presenting it to the user.

FIG. 19 is a block diagram of an information exchange system with aMaster Agent (SIE, CIE, DB, WS), transmission lines, and user agentswith diagram information propagation sequence.

FIGS. 20A and 20B are a flowchart of an exemplary method for moving datafrom short term memory to long term memory and “forgetting” (e.g.,discarding) data.

FIG. 21 is a flowchart of an exemplary method of agent recordingconsumption information and altering internal relations.

FIG. 22 is a flowchart of an exemplary method of ranking data objectsbased on data object type.

FIG. 23 is a flowchart of an exemplary method of using a neural network(NN) to optimize an algorithm with old consumption data.

FIG. 24 is a diagram of an exemplary hierarchical music genre taxonomy.

FIGS. 25A and 25B are a flowchart of an exemplary method for coldstarting recommendations.

FIG. 26 is a flowchart of an exemplary method of replacing an ad for auser in a webpage or video

FIGS. 27A, 27B, and 27C are a flowchart of an exemplary method ofoperating a decision engine.

FIG. 28 is a block diagram of an exemplary computing device.

DETAILED DESCRIPTION

The following abbreviations and exemplary definitions are used todescribe some embodiments of the disclosure.

ACL=Access Control List

API=Application Programming Interface

CA=Contact Agent

CIE=Collective Intelligence Engine

DB=Database

DE=Decision Engine

DO=DataObject

FOAF=Friend of a Friend

FSM=Finite State Machine

LTM=Long Term Memory

MA=Master Agent

NN=Neural Network

NOA=Network of Agents

OSA=Original Sending Agent

P2P=Peer to Peer (or Agent to Agent)

PA=Proxy Agent

PSTU=People Similar to you

RPA=Randomly Paired Agent

SIE=Semantic Intelligence Engine

SOPC=Self-Organizing Preference Component

UA=User Agent

UGC=User Generated Content

UI=User Interface

WS=Web Server

Anonymization network—A network in which the transmission of informationis transferred between proxies in such a way as to enable internetanonymity of the original sender from the perspective of the finaldestination node.

Autonomous Agent—An autonomous agent that can operate based on input andother environmental stimuli, not just on explicit user commands alone.Autonomous agents can have different degrees of autonomy. Some agents donot get help from a central point or a network of other agents to assistin information gathering, transmission, and processing tasks. Somecommunicate sporadically with other agents and central points. Stillothers rely on shared network duties between other agents and centralpoints for proper operation. In some fashion, autonomous agents gatherinformation, process it, and make decisions or perform tasks for itsuser. Autonomous agents keep an internal routing table to communicatewith information sources and other agents. They do not necessarily needto use a central point as an index to locate and communicate with otheragents; however, this does not exclude autonomous agents from using acentral point from finding another network entity. Weakly autonomousagents rely on a central point like a master agent for networkinformation and intelligence updates but executes intelligence andinformation ranking of DataObjects itself. A strongly autonomous agentis a self-sufficient agent that gathers intelligence and information byitself on the internet. Although a strongly autonomous agent doesn'tnecessarily need updates from a master agent, it can exchangeinformation by communicating in a P2P manner with other user agents andmaster agents.

Browser—A computer program that displays web content for a user

Client-Server Architecture—A centralized network architecture in whichusers on a network called clients make requests to servers, which hostwebsites, for information that is stored on the server. The serverresponds to the client by sending back the requested information.

Concepts—A distinctly classified space of ideas and/or things

Consumer—A user or a user's agent that consumes information

DataObject—A document, body of text, image, or other collection of data,such as a story, advertisement, event, restaurant information, retailsale or coupon, message, phone call (e.g., an audio recording ortranscript), file, webpage, video, audio recording, TV Show, movie,product, person or network user profile, or any source of information.DataObjects include, but are not limited to, descriptions of a person,place, or thing. Each DataObject is associated with a location fromwhich the DataObject may be accessed. For example, the location mayinclude a uniform resource identifier (URI) (e.g., a uniform resourcelocator and uniform resource name), a shared storage location, and/orany other address at which a computing device may access (e.g.,download) the DataObject.

Entity—Something that is real and exists or has existed in the past suchas a particular person, place, or thing.

Environment—Any external information domain of data that is input to thelearning system.

Environmental Stimuli—Data object information within the environmentthat the system consumes. This is any information that can be reduceddown to text including voice.

Information Exchange—Information that is exchanged between two parties.It could be semantic-type information about DataObjects, commands forsharing information with others or distributed computing commandsbetween agents.

Intelligent Agent—A computer program acting on the behalf of a user. Ithas the ability to understand semantic data. Based on input data, agentscan make decisions and predictions for its user. It can perform tasksfor the user and present the user with new and useful information.Intelligent agents have different degrees of autonomy.

Network Supporter—Node on the network that contributes to the flow ofinformation. Such a node could be a website that stores information(online drive), a node that specializes in streaming information toother nodes, or a node that does distributed computation for agents.These nodes could be user agents, a master agent, or special programsand network resources set up to interact with the system's flow ofinformation.

P2P—Peer-To-Peer or Agent-To-Agent

Passive Consumption—Consumption by a user in which information isimplicitly brought and presented to the user, such as by an intelligentagent. In such a scenario the user does not have to explicitly go outand find information manually in order to consume it.

Producer—A user or user's agent that produces information such as astory, webpage, video, etc. Websites are also considered producers forthe information that they produce.

Recommender system—A computer program that tries to recommendinformational items such a films, television shows, music, etc to aninterested user.

Secure transmission—Encrypted transmission between two parties that iscommunicated in such a way that a third party cannot listen in andunderstand the transmission.

Semantic data—Information describing the essence or content of aDataObject. In exemplary embodiments, semantic data associated with aDataObject includes entities (e.g., people, places, times, and/orevents) referenced by the DataObject, as well as relationships betweenthe referenced entities. For example, a DataObject may indicate that aparticular person became the head of state of a particular country on aparticular date.

SPO—Subject-Predicate-Object grammar statement

TOR—A network of nodes set up to anonymize user information such as thelocation of a user and mask other factors that could identify them. TORstand for “The Onion Router” because information is packaged inencrypted, layered envelopes where proxy nodes can only decrypt onelayer telling them how to route the message while not giving access tothe message payload.

User Interface—A computer program that controls the display for a useron a viewing device like a computer monitor and allows the user tointeract and give commands to computer programs.

User Preferences—Information of what a person does in life and theiroverall personal composition. This information could include informationconsumption, activities, feeling and emotions, things related to theperson such as affiliations, and physical and mental characteristics ofthe person.

Exemplary embodiments provide one or more of the following features:

1. A generalized intelligence that handles any problem space to makedecisions. Conventional recommender system algorithms to solve specificproblems in small problem spaces. The system described herein, alsoreferred to as a “construct”, adapts to the problem space by its ownactions and with help from the SIE. Conventional systems include hardcoded, unchanging programs specifically coded for their particularproblem spaces.

2. Decision models allow the construct to make decision and takeactions—including a recommendation—but could be an action based onaction model, such as transmitting a notification or playing an audiotrack.

3. Automous user agents learn on their own, sharing with each other in apeer to peer manner and/or with central points, amalgamating experiencestogether to become smarter by new intelligence and learnings sharing.Each agent can re-assemble dimensions with new added or deleteddimensions and still make decisions, assess how that affects success orfailure, and adapt decision models accordingly if advantageous.

4. Advertisement personalization for an advertiser/producer whilekeeping advertising selection and/or viewing private from theadvertiser/producer. Accordingly, the system may operate as a securemiddle man between the producer, the ad network, and the user

5. An ability to passively retrieve DOs/ads for the user while keepinguser information private from central points (e.g., the master agent)and/or untrusted parties.

6. An ability to passively share DOs with other users without explicitaction. This is in contrast on how people share information using socialmedia, where the user must take explicit action, like clicking a “share”button, to share content. Agents may automatically share consumed DOs(if user permissions allow) with each other and filter if those sharedDOs are shown to a user based on user preference. Sharing and filteringare performed transparently to the user, such that the user sees onlyDOs determined to be relevant.

7. An ability for anyone to produce DOs, get those DOs to interesteduser (passively), and earn money from the DO with personalized,privatized ads.

8. An architecture in which an SIE performs intelligence processing(e.g., extracts semantic data from a DO) and then sends semantic data toa client to make a decision (on the client processor) about which DOs topresent to a user. Analysis is performed by an SIE type program, andcondensed essence list is sent from the SIE to a client program, whichperforms personalized decision making to recommend a DO, take actionwith respect to the DO, or do nothing, based on the semantic dataassociated with the DO.

9. The state of the user agent may be copied (e.g., synchronized)between devices. Attributes used by the user agent, such as depth ofcomputation, DO types to access, etc., may be specified per device.

10. Dimensions of a problem are determined by an SIE like device, whichcreates a decision model and sends the decision model to a client. Theclient can also determine new (and/or delete, and/or change weightingsof) dimensions in a problem space as the context might be different fordifferent users or client computers. As an example, a desktop agent canrequest and get more information like elements within the operatingsystem (OS) file system, how the elements are related to files, whatfiles are and contain, etc., for the local agent to determine how suchinformation may be relevant to a user of the OS.

11. Condensed essence lists may include advertisements.

Specific examples are used herein to describe the function of exemplaryembodiments. Accordingly, references to “the system”, “this system”, and“the construct” are exemplary only.

Semantic Internet Logistical Information Exchange System

In exemplary embodiments, agent programs perform information gathering,information transmission, and other tasks on behalf of users. In orderto perform the tasks, the agents have some level of intelligence toefficiently interact with the vast environment of the World Wide Web.Agents not only interact with data sources but also with other agents.Agents can share data and intelligence with each other. In this mannerthe semantic web is slightly different that the current, mostlycentralized internet where browsers are powered by explicit usercommands to consume data in a client-server fashion.

Semantic tools referred to herein are capable of describing relationswithin text that a computer program can understand. In exemplaryembodiments, methods described herein employ a “top-down” approach tothe semantic web. The top-down approach may be understood as a scheme inwhich a semantic computer system retrieves and consumes certain types ofinformation. This information is processed and exposed, possibly insemantic formats, to external systems through a web service or anApplication Programming Interface (API). Exemplary embodiments employ atop-down approach for gathering and semantically processing data beforetransmitting processed data to a network of agents.

The theoretical view of the semantic web is an internet that is moredecentralized and is based on intelligent agents. Agents can shareinformation and intelligence with each other in a peer-to-peer (P2P)manner without going through centralized points like websites. Becauseagents are semantically intelligent, websites, and the internet itself,become more of an open database where agents query sources by consumingthe content, analyze the content for its semantic value, and performsome useful function on the content for the user. The useful functionsof agents could be as simple as recommending content to a user forconsumption. In this way agents make decisions for the user. Theypredict what the user would like to consume.

In exemplary embodiments, agents are not required to performcomputationally intensive tasks, such as individually gatheringinformation from myriad sources on the internet and then classifying thedata. Rather, such tasks may be performed by a centralized master agent.Further, because processed data is provided to decentralized agents bythe master agent, the need for agents to rely upon other agents toprovide such data is obviated. Accordingly, exemplary embodiments reduceor eliminate the need to synchronize this data among an entire system ofdecentralized agents.

Exemplary embodiments use a central point for gathering and distributionwhile allowing agents to work semi-autonomously. Agents are thus freedfrom the responsibility of having to autonomously query the internet forinformation or rely on other, connected agents for new informationupdates. Computation by agents may be concentrated on processing datafor users, giving the user a better internet experience while optimizingthe use of network and local resources.

The essence of the semantic web is bringing meaning and order to theinternet's information. Once agents understand the meaning of theinformation they consume, they can bring information to users. Theseaspects of the disclosure allow the logistical networking means for sucha scenario. The scenario may be understood as a “passive” internetexperience where users no longer have to explicitly go out and findinformation on websites. The information they are interested in isbrought to them. This system also helps to negate the problems ofinformation overload and “bad” information hits (e.g., falselyidentifying irrelevant information as relevant). Highly probable, “good”information hits are presented to users by their filtering intelligentagents. This system provides an infrastructure enabling such an improveduser experience.

Users generally have little control over third party information sharing(e.g., among websites) and are usually unaware of it. Further, somecomputer systems request account credentials from a user to accesscontacts and/or other information from a corresponding account. From asecurity point of view, sharing credentials with third parties isextremely insecure. Exemplary embodiments operate without sharing userbehavior information between websites and without requiring a user toshare account credentials between websites. For example, exemplarynetwork architectures support the network agents' ability to keepdesired user information private and away from centralized points likewebsites and advertising networks.

For optimization purposes, exemplary embodiments allow bothdecentralized and centralized information transmissions. A master agentholds collective network information and intelligence of the system. Themaster agent updates user agents with updated network informationthrough a propagation scheme. Then user agents can propagate theinformation to other agents throughout the network. As mentionedearlier, it's computationally intensive for a user agent to consume allthe information on the web, break it down, process it, and make ituseful for user presentation. The client resources of memory andpermanent disk space have to be extremely large. It's more efficient andlogistically feasible to have a master agent that does most of the workbefore sending the digested knowledge to user agents. It's also moreefficient for user agents to aggregate information on a master agentthat can collectively process, store, and retransmit the updated data.

Because the master agent takes the responsibility of gatheringinformation, classifying it, and then disseminating it to a network ofagents, it can directly connect producers of information with the likelyconsumer of that information. Central points, like websites, becomeoptional as agents exchange information in a mesh of connected consumersand producers. Producers can be central points, such as websites, orother network agents whose users produce user-generated-content (UGC).UGC may be stored within a central point because of the predominantclient-server network architecture of the internet. This causes problemsin the continual support and production of new information. Centralpoints usually get the majority, if not all, of the advertising revenuefrom the contributions of others. Exemplary embodiments enable aninternet marketing model in which producers of content are directly paidbased on the popularity of their contributions since in most cases thereis no need for a middle man.

Methods and systems described herein facilitate simplified distributionof information to interested parties via agents. For example,DataObjects may be disseminated through the agent network. In someembodiments, DataObjects include advertisements, and an agent may selectthe advertisements that are determined to be most relevant for the userof the agent. This mechanism provides a marketable vehicle foradvertisers, thereby encouraging the continual production andpublication of DataObjects by network producers. Accordingly, agents mayprecisely target people yet not infringe on their privacy. Aninformation exchange structure allows agents to provide a passiveentertainment experience, similar to TV, for their users.

In exemplary embodiments, no source or producer on the network receivespreferential treatment or has an unfair advantage because of size,leverage, user base, or capital. Rather, all producers are equal andhave an equal opportunity to produce information and to publish thatinformation on the network so others can consume it. Because the agentpulls information instead of the user needing to know where to go tofind information, which is usually the sites with the biggestadvertising budgets to reach users through brand recognition, a fairermethod of competition is provided for information exchange. For example,a local classified advertisement of a garage sale has just as muchpotential visibility as a news story from a major news outlet because ofthe ability the system gives agents to pull and rapidly assessinformation.

In some embodiments, agents work together in a collective, swarm-likemanner. The agents can share information, the distributed processing ofinformation, storage of data, and other logistical networkresponsibilities in cohesion. The system also allows agents to shareintelligence for collective learning which can be efficiently aggregatedand processed on the master agent. Agents can work together in ananonymity network (e.g., similar to TOR) to anonymize the source of arequest for information, thereby enabling a withholding of the user'sidentity and consumption from centralized points. The system has ascheme to accept anonymized consumption reporting yet verify thatreporting sent in is correct and has not been changed by maliciousagents on the network.

Exemplary embodiments provide one or more notable features, as describedbelow.

1. Support for seamless sharing of information where pertinentinformation desirable to users is retrieved autonomously on theirbehalf. Agents associated with similar users are enabled to shareinformation with each other so that users can consume new and usefulsources of information that they would not have done so otherwise.

2. Support for agents that are stateful and keep user informationprivate. This system works with agents to validate consumption data fromagents without infringing on user privacy rights. Through privatetransmissions between agents, the system has a collective memory of whatall users of the system are doing on the internet, even within silos.This facilitates collaboration, new information discovery, and increasesin collective learning.

3. The current internet client-server infrastructure is difficult tocreate sustainable business models by marketing to internet users. Thecurrent internet architecture makes it practically impossible toeffectively market to users without infringing on the privacy rights.This system gives intelligent agents the infrastructure and support toincrease the business value of the internet to marketers withoutinfringing upon user privacy rights. This system provides the mechanismfor marketability by sending out advertisements that are semanticallyclassified for agents to process so that each user can be bettertargeted in a private manner.

4. The system allows a global collective memory to emerge based on theexperiences of each user agent that is aggregated to the master agent.Currently most semantic data is manually created by a human for verysmall, local knowledge areas such as a webpage. There is no system thatcan tie all these local areas of knowledge together for a bigger, moreintelligent viewpoint of the internet, its information, and how it issemantically related. This system can accomplish these tasks with itsnetwork infrastructure. It provides a master agent and a user agent alogistic and efficient means of transferring information. Some examplesof collective knowledge that can be learned by the system: male teenslike to watch violent movies; more women go to church than men; 25% ofusers are not planning to vote in an election where candidate x has 65%of the vote of known voters; financial professionals like to read theWall Street Journal; 21-34 year-olds are most likely to go to bars andnight clubs on Friday and Saturday night over any other demographic.

5. Through support of information gathering and dissemination duties,the system makes it feasible for intelligent agents to operateefficiently and brings about a purer vision of the semantic web wherebottom-up and top-down approaches have been nonexistent or have failedto materialize all together.

6. Through its coordination of agent information propagation anddistributed computing, the system maximizes network resource allocationand usage. It assists agents in using each other to better serve theirusers. Because resources are used more efficiently, the system uses lessoverall resources, especially at central points, for informationprocessing and distribution.

7. The system supports agents in giving users a passive internetexperience by the continual distribution of pre-processed and summarizedsemantically described DataObjects. This allows agents to expend most oftheir energies analyzing just the condensed DataObjects that they aresent for presentation to the user. This encourages the frequency ofuseful, good information hits to the user without overloading the userwith information. Current trends on websites, especially social mediasites, leverage their user base to transfer information. Information isvirally disseminated between site contacts with little or no analysis ofhow the information pertains to the user. Massive amounts of poorinformation hits are presented to the user, degrading the userexperience.

8. Systems as described herein can be used to help to solve copyrightinfringement issues. The system can calculate the similarity ofDataObjects through a comparison of the semantic composition betweenDataObjects. Whether published and/or hosted by some party on theinternet, the system can make sure to notify the correct copyrightholder of the issue and make sure that monies earned from consumption ofthe DataObject are received by the correct party.

9. The system facilitates eliminating unfair advantages to informationproducers that reach audiences simply because of market position andbrand recognition alone. The system enables equality in thedissemination of information and ability to reach users. Each contentproducer may have equal means to publish and disseminate information.Anyone has the ability to produce DOs, get those DOs to interested userspassively through the network of agents, and make money off the DOs withpersonalized, privatized advertisements. The system will automaticallypick up newly produced information, analyze it, and send it out to thenetwork of agents where people interested in it will be able to consumeit. This more passive experience is in contrast to current systems ofproducers explicitly having to put information on websites and consumersexplicitly having to go out and find the information to consume it.

10. A network of agents as described herein may make the web moremarketable as people can anonymously be targeted without tracking,thereby abiding by user privacy rights and/or preferences. The MasterAgent has the ability to work with the network of agents to keep userdata private. In particular, personalized, private advertisements thatare semantically analyzed, delivered, and reported back to central pointin an anonymous way. The anonymous advertising may allow personalizedmarketing without infringing on user's privacy and a reporting schemethat can be verified as accurate without revealing the consumer of theadvertisement or infringing on a user's privacy. If there are maliciousnodes on network they can be identified and ostracized. Individualpreferences are not aggregated on a central point away from usercontrol.

11. The system architecture is conducive to supporting traditional formsof media on the internet. For example investigative journalist canpublish stories and still reach an audience. They directly benefit basedon consumption and can continue their art without the need of supportfrom a major media outlet.

12. The intelligence of the system may increase over time based oncollective knowledge aggregated on the master agents from the network ofuser agents it serves information to.

13. Since the system is stateful, it can help to minimize the impact andprevalence of malware and other malicious content introduced into thesystem by actively seeking and ostracizing the producers of suchcontent.

14. The system enables the production of relevant, serendipitousinformation hits for users. It can find better (e.g., more relevant),newer information based on collective data consumption habits and usersimilarity comparisons. Users have the chance to consume informationthat they wouldn't have seen or consumed alone.

15. The system is conducive toward the emergence of collectiveintelligence and problems solving through the use and dissemination ofdecision models between network agents and the Master Agent.

16. The system is conducive toward collective, swarm-like behavior toshare network responsibilities between network agents and a MasterAgent.

17. The system architecture allows the SIE to do the heavy intelligencecalculations on analyzing the meaning of DOs and creating decisionmodels. With the help of a MA, lists of the semantic essence of DOs,including advertisements, as well as decision models are sent to clientagents where decisions are made on whether or not to take an action suchas recommending DOs.

FIG. 1 is a block diagram of an exemplary information exchange system100 including three parts, which may take the form of executablesoftware components. The first part is of the system is the Master Agent105, which may be abbreviated as “MA”. The master agent 105 aggregatessystem knowledge and intelligence and is the central point of thesystem. The second part of the system is the transmission schemas 110 ofinformation to and from the user agents 115. The transmissions 110 arepackaged correctly for agents to understand and compact enough for quicktransfer over the wire. The last part of the system is a network ofintelligent agents 115 that receive information transmissions from themaster agent 105. These agents 115 work on behalf of their associatedusers and may be referred to as user agents 115, abbreviated as “UA”.The master agent 105 supplies the agents 115 with packaged, semanticdata such as webpages, movies, advertisements, and also includedsemantic intelligence itself. These agents 115 in return work with themaster agent 105 to report user actions and consumption history in aprivatized way such that the collective intelligence of the system canincrease over time without disclosing details of individual userbehavior.

By having these three components working together an effective system ofinformation and knowledge transfer is provided. Using a central point(e.g., the master agent 105) in coordination with a network of agents115 means that the agents 115 do not have to perform redundant datagathering and analysis. A waste of time and resources would occur withdecentralized agents working autonomously to query the informationsources on the internet. Logistical problems also occur in a network ofagents working together to gather and process information. Synchronizinginformation and maintaining network state is difficult and resourceintensive. By using a master agent 105 that specializes in informationgathering, semantic processing, and transmission, it is more efficientthan having a myriad of agents doing it autonomously or working togetherin a decentralized manner.

The components can facilitate the emergence of auxiliary properties ofthe system. A system acts as a framework of information and intelligencewhere the production of information can accurately and seamlessly findthe correct consumers for it. This architecture leads to otherbeneficial characteristics as well such as keeping user informationprivate while allowing for a more personalized, yet anonymous, marketingapproach for information producers.

Master Agent

The master agent is different from a user agent. In exemplaryembodiments, the master agent is associated with greater computationaland network resources than those associated with typical user agents.Referring to FIG. 1, the master agent 105 includes a web server (WS)120, collective intelligence engine (CIE) 125, and semantic intelligenceengine (SIE) 130. The WS 120 takes in information from and sendsinformation to network user agents 115. Network agents 115 cancommunicate with the master agent 105 in a centralized manner throughthe master agent's WS 120. A database 135 stores data provided by,and/or retrieved by, WS 120, CIE 125, and SIE 130.

The web server portion of the master agent acts as the internet or agentnetwork facing part of the system. It acts as a central point that sendsand receives messages from agents, keeps network state for agents whilethey are offline, and coordinates transmissions and networkresponsibilities between agents. The WS keeps track of agent's userDataObject consumption and explicit user actions such as commenting on,sharing, or rating DataObjects.

The WS is the point of service for agent queries for information theydon't know. When agents are unable to find answers from connectedagents, the master agent can be called upon to solve a knowledgeproblem. An agent may have never encountered a certain piece of semanticdata that makes up a DataObject. In order to process the DataObjectcorrectly, it can query the master agent for all metadata andintelligence relating to the new piece of data.

The master agent keeps network state for the agents while they areoffline. The WS is responsible for updating agents with DataObjects andmessages sent to them when they come back online. Once online theinformation stored temporarily on the master agent is retrieved and sentto the newly online agent. It keeps a record of new DataObjects thatoffline agents miss.

The master agent can work and coordinate tasks for network agents. It isbeneficial for agents to work in coordination with each other or amaster agent for network cohesion. If they work together and shareresponsibilities then the users of agents can benefit with betteroverall service from their agent. For example the master agent can pairtogether two agents that do not know each other but are online atopposite times of the day. Therefore one agent can do the semanticprocessing of DataObjects for the other agent while it is offline. Oncethe offline agent comes online, there is not a DataObject processingdelay in presenting DataObjects to the user. These agents will trade offprocessing duties with each other while the other is offline to increasethe user experience for both of their users.

The web server works in coordination with a CIE. The CIE records useraction and consumption information sent in from agents. Collective datagoes to the CIE for processing. The CIE processes the information toform a collective knowledge base that makes the whole network smarterand better at working together. The CIE determines similarity betweenagents on the network, similarity between DataObjects on the network,DataObject popularity, global user sentiment toward concepts andDataObjects, and “hot spots” where people and agents are aggregatingaround the internet for data consumption. The CIE records and calculatescollective metadata about DataObjects such as which are most popular,top rated, shared, etc. The CIE will package data based on itscalculations so that it can be output to the agents. The packagedinformation is sent to the WS to be sent out with other DataObjecttransmissions.

Sometimes agents discover new information that they can share with theCIE, a special function of the CIE is analyzing new knowledge sent to itby agents. These new insights range from new semantic relations to newwebsites and producers. It can record new semantic knowledge learned byagents and analyze the knowledge for its collective value. If enoughagents in the field observe or learn the same knowledge it will reach alearning threshold, and the CIE can pass on that knowledge to the SIE tobe incorporated into its intelligence database (e.g., in database 135).The CIE can also pass it on to other agents in the network that maybenefit from it. In this way a network of agents can learn and becomesmarter over time. The new, collective knowledge of agents populates andaugments the SIE's central knowledge base over time. The CIE supportsboth supervised and unsupervised learning schemes so that anadministrator can approve of new system knowledge before adoption.

The WS and CIE together are able to semantically analyze knowledge andDataObjects. They work in conjunction with an SIE. Together, thesecomponents allow the master agent to act as a bridge between informationproducers and information consumers. It can implicitly retrieve datafrom producers such as websites or receive data that is explicitly sentto it by agents such as user generated content (UGC). The data isanalyzed by the SIE for its semantic value. The SIE is responsible forunderstanding the information that producers on the network create sothat it can be sent back out to user agents to process further andpossibly recommend to their users.

After the DataObjects are analyzed by the SIE, their semantic content isextracted and summarized so they can be compactly packaged into a lowbandwidth version. Before the WS can send out the DataObjects, the CIEcalculates the similarity between DataObjects on the network so thatagents can avoid presenting redundant DataObjects to users. The WS getsthe new streams of DataObjects and distributes them to agents on thenetwork. This compressed version allows thousands of DataObjects can besent out together to agents for further processing.

Exemplary embodiments perform information discovery, semantic valueprocessing, packaging, and distribution at a central point. Anautonomous agent or completely decentralized network of agents couldaccomplish the same tasks but it would take massive amounts of computingpower on the client computer, an extremely complex information syncingalgorithm between all the agents, and high amounts of bandwidth.Therefore, a central point for such a semantic network frees up the useragents to spend their time evaluating information for the user.

System Transmissions

The second part of the system is the transmission schemas (shown astransmissions 110 in FIG. 1) and methods that allow the system totransfer information effectively in a timely, resource-friendly manner.Although most websites at present relay and send all information to theuser, this involves a substantial quantity of web servers, processingresources, and bandwidth. This system facilitates effectivelytransferring information without requiring nearly as many resources peruser supported in the network. It does this by propagating informationthroughout the network instead of one-on-one transfer from each user tothe central point.

FIGS. 2A and 2B are a flowchart of an exemplary method 200 by which amaster agent communicates with a user agent for providing DataObjects tothe user agent. In exemplary embodiments, repeatedly (e.g.,periodically, continually, and/or upon request), the master agent willtransmit information to network agents. For example, the user agents mayperiodically (e.g., several times per hour) request new DataObjectinformation from the master agent. This information includes newDataObject metadata (e.g., locations, such as uniform resourceindicators, and semantic data), updated metadata for old DataObjects,and newly discovered collective and semantic knowledge. Rather than makethe same call to each user agent, the master agent can coordinate thepropagation of information so that agents can effectively transfer thenew information to other agents on the network. FIG. 3 is a diagram 300of information propagation in information exchange system 100 (shown inFIG. 1). FIG. 4 is a flowchart of an exemplary method 400 forcentralized DataObject information propagation. This process maycontinue until all online agents have been updated. In this way thesystem leverages other agents on network and their resources to worktogether in order to send information quickly and efficiently in a P2Pmanner. Therefore, no massive network infrastructure is needed for themaster agent. This swarm-like exchange allows each member to pass oninformation to its neighbors and keep the whole network in the sameupdated state. Not all categories of information are necessarily sent toeach agent. To further the economy of transmission the system sometimesuses an algorithm to form mini-networks and make sure agents that sharesimilar information consumption preferences propagate information witheach other. This allows for a minimization of bandwidth usage and strainon the network.

In some embodiments, the system compresses the information transmittedwhen the information is packaged for further increases in efficiencies.FIG. 5 is a flowchart of an exemplary method 500 for distributingDataObject information by the master agent. FIG. 6 is a diagram 600 ofan exemplary centralized model of DataObject information propagation.The informational and intellectual essence of each DataObject issemantically summarized and packaged together. This allows the system todisseminated large amounts of DataObjects with every interval'sinformation propagation. The system facilitates a well developedperspective for user agents on what is being produced and consumed onthe internet as a whole. This information compression allows user agentsto quickly process DataObjects without having to traverse andsemantically classify the full breadth of each DataObject. The mostimportant parts of each DataObject, such as semantic essence andrelations, location of the DataObject, collective metadata likepopularity, are sent in the propagation stream allowing quick rankingand recommendation to users from network user agents.

Using a master agent as a central point with the SIE allows fortremendous savings in CPU usage, bandwidth, and overall networkresources. In this manner the system is a hybrid P2P and centralizedsystem using the best logistical features of both network architecturaltransmission methods. Using just one or the other may lead to decreasesin efficiency, increase the system resources, and/or diminish userexperience overall due to lack of prompt DataObject delivery updates.

Alternatively agents can discover new data objects without being part ofthe propagation network. The system also publishes RSS feeds about newDataObjects for different categories and knowledge areas. Thisdissemination of information is similar to how torrent networks publishdata. Agents can consume the RSS feeds, rank which DataObjects theythink the user will be interested in, and retrieve those DataObjects.Some agents will become trackers that assist the Master Agent andnetwork agents in keeping track of which agents have which DataObjects.The tracker helps to keep state of which agents have which parts of thefile so it can be shared rapidly within the network. This approach maybe used for sharing DataObjects that are large in size such a moviefiles. An agent can find out who the current trackers on the network arefrom the torrent file for a particular DataObject. An agent can thencontact those trackers to see which agents have those DataObjects. Theagent can then request parts of the DataObject from multiple agents toretrieve it more quickly than requesting from one agent alone. Once theagent has all or part of the DataObject it can share it with otheragents on the network as well.

For additional security information and data can be sent in a TOR stylemanner where it is sent in layered, encrypted envelopes so that proxyagents can only decrypt one layer giving the proxy agent instructions onwhat action to take such as instructing it on what node to next send theinformation. This allows further anonymization among nodes and centralpoints as well as the ability to save data such as personal, importantmessages or other private data on a central point where the encrypteddata can be stored and later relayed to the message receiver when theycome online again.

In addition to DataObject, the MA also disseminates decision models tothe network of agents. The Semantic Intelligence Engine (SIE) remotelybuilds global semantic intelligence outside of the intelligenceconstruct. Through the Master Agent the SIE updates network constructswith decision models. The Decision Engine (DE) within the intelligenceconstruct uses decision models to make decisions based on DataObjects'semantic essence data sent to the construct via the Master Agent anduser actions over time (recorded locally by the construct). The twoparts of the SIE and DE work together.

The decision models are sent in such a format so that for any type ofdecision the agent has to make, the proper dimensional data types,weightings for those dimensions, and sequence of processing is availableto plug into the decision. All decisions are made based on what the SIEdetermines is relevant for a given contextual problem space and has anaction associated with it such as making a recommendation. The SIEdevelops models for these based on experiences it has with data itencounters. The internet and network of agents act as the domain of itsexperience; however, with more data, like sensor data, this could beextended into the physical world as well. These models or experiencescan be shared with local user agents. The network of agents can alsofigure out new significant dimensions based on their own experiences andnumber crunching. These local experiences can be sent to the SIE to docollective computations and processing. If relevant to the network as awhole these local experiences and learning will be globalized on thenetwork and sent out to other agents to assist in their decisionprocesses. The domain of semantic data is covered specifically but thisprocess and decision models can be used to make decisions over anyproblem space, even for problems in the real world.

Network of Agents

The last part of the system is the logistical information exchange to,from, and within a network of user agents. The system uses centralized(e.g., as shown in FIGS. 4 and 6) and/or decentralized (e.g., as shownin FIGS. 7 and 8) information propagation. FIG. 7 is a flowchart of anexemplary method 700 for decentralized DataObject informationpropagation. FIG. 8 is a diagram 800 of an exemplary decentralized modelof information propagation.

User agents can be weakly or strongly autonomous in the system. Weaklyautonomous agents rely on a central point like a master agent fornetwork information and intelligence updates but locally executesintelligence and information ranking of DataObjects itself. A stronglyautonomous agent is a self-sufficient agent that gathers intelligenceand information by itself on the internet. Although a stronglyautonomous agent doesn't necessarily need updates from a master agent,it can exchange information by communicating in a P2P manner with otheruser agents (e.g., FIG. 7-D) and possibly master agents.

User agents are intelligent agents that do work on behalf of their user,whether or not they are strongly or weakly autonomous. They work onbehalf of the user to gather data, process data, determine which datathe user would be interested in consuming, and presenting that data to auser. This includes finding stories, syncing and sharing data like filesbetween each other, or downloading new music for the user. It should benoted the system itself does not include the intellectual property forsuch an agent; however, the aspects of the disclosure are a system ofinformation exchange between such agents.

The master agent allows the network agents to communicate in a securemanner. All system transmissions, including transmissions between themaster and user agents and also transmissions between user agentsthemselves may be encrypted. The system supports a network of agentsthat use a public/private key encryption scheme. FIG. 9 is a flowchartof an exemplary method 900 of a master agent operating as a certifyingauthority in secure communication between user agents. FIG. 10 is adiagram 1000 of cryptographic key exchange.

For agents to be able to communicate securely in a P2P manner, they mayhave some way of verifying that the other agent they are trying tocommunicate with is the actual agent and not an imposter. The masteragent acts as the certifying authority for agent identification. Ascertifying authority it authenticates the identity of each agent beforethey can communicate over an encrypted stream. Once the master agentconfirms that both agents really are who they say they are it providesthe public keys of each agent to the other. This allows each agent tothen decrypt messages from the other agent while making sure that it iscommunicating with the correct agent on the other end. If there areagents in the network trying to masquerade as an agent different fromthemselves, the system can record their malicious activities andostracize rogue agents from the network. It will send out warnings sothat valid agents are aware of network elements that should be avoided.

The system supports anonymized transmissions from agents. FIG. 11 is adiagram 1100 of an exemplary model of an anonymization network of agentsconcealing private information like ads the user consumes and validationof reporting with master agent. In the exemplary embodiment, Agent Areceives 1101 data object information from Master Agent, processes it,and User consumes it. The Consumption is relayed 1102 to CA, RPA (AgentD). Agent D reports 1103 step 1102 to Agent E (Reporting step 1102 goeson an indefinite amount of times depending on security settings of theagent). Agent E reports 1104 step 1102 to agent F. Agent F reports 1105consumption information to Master Agent. A confirmation is sent 1106back from Master Agent that message was received. Successfully receivedmessage is reported back 1107 from MA to Agent F (the reporting agent).Step 1107 is repeated 1108 and 1109 by agents E and D to OSA. OSA sends1110 verifiable message of consumption to agent G with reporting agent'sID (Agent F). The message is sent back 1111 to Master Agent (Relay cango through N number of Agents). The Master Agent checks 1112 forredundant messages. If redundant messages exist, do nothing. If noredundant messages exist, start the resolution process

FIG. 12 is a flowchart of an exemplary method 1200 of anonymizationconcealment between agents and validation with master agent. Agentswithin the network from a network of proxy agents like an anonymizationnetwork to report consumption of other user agents without revealing theoriginal sender of the transmission. The original sending agent willsend detailed information such as web page x was consumed or DataObjecty was rated 5.0 through one set of proxies. Since anonymizedtransmissions go through a proxy agent, the master agent has no way todirectly verify the original message is indeed the message that itreceives.

FIG. 13 is a diagram 1300 of an exemplary model of anonymous consumptionreporting error resolution for verification purposes. As shown indiagram 1300, an additional message is sent by the original messagesender to a second proxy so that the master agent can verify thatmessages have not been altered by malicious agents. If inconsistenciesare found an error resolution process takes place to determine the badrelay node on the network. Specifically, if there is no redundantmessage, the Master Agent (MA) begins a resolution process to find badnode in the network. MA sends back 1301 tracking messages to agents tofind what other agent sent a redundant message. Original Sending Agent(OSA) will tell D in step 1305 it was agent G. G will be reported backto Main step 1108, a message is incrementally relayed back again 1306,1307, and 1308 between agents. MA contacts 1109, 1110 G to see where itsent the message. MA knows 1111 based on where each message was lastreported in the relay which is the bad node. If G says, it sent it toMA, G was the bad node. If it sent to H then H was the bad node. If Hsays it never received it then G and H both are suspected. Over timewith repeated violation, G or H will be found to be the bad node andpossibly put into a “probation” or “ostracized” status within thenetwork for not sharing information exchange responsibilities.

Note: MA reports consumption statistics to websites/producer to makesure consumption statistics are correct.

System Properties

The system supports these agents with information and gives them a wayto quickly process DataObjects. It provides a logistically andcollectively easier method for agents to work and share informationtogether. The system infrastructure allows additional beneficialproperties to emerge due to the ease with which information is createdand disseminated. One property the system provides is a network betweenproducers and consumers of information. Some network nodes, such as awebsite or a user agent, produce (e.g., publish) information while othernodes consume (e.g., download and/or present) it. FIG. 14 is a diagram1400 of an exemplary model for connecting producers and consumers ofinformation. In the exemplary embodiment, model 1400 includes the MasterAgent retrieving 1401 new DataObjects. User Generated Content is 1402input into the system. The Master Agent processes 1403 the DataObjects.The DataObjects are distributed to a Network of Agents. Local agentsretrieve 1405 the DataObjects from websites. The DataObjects arepresented 1406 to a user.

The system connects producers and consumers of information together. Thesystem does this by allowing producers to expose DataObjects to thesystem and allows those DataObjects to be distributed to people who wantto consume them. After a producer creates content, such as a story, blogpost, photo, video, or any other piece of data, the master agent willretrieve the new content, whether it is stored on network-based media oron the producer's local computer, consume it, process it, and send outits semantic value to other agents. DataObjects can be explicitlyexposed by producers or the system can passively consume and analyzethem if user permissions allow. This is in contrast to websites that arelimited to explicit user data population. The system being describedwill auto-populate itself with UGC and send the UGC to others on thenetwork. This does not mean all DataObjects or user actions arenecessarily shared publically. Rather, the system enforces preferencesof user agents for data syncs so that just certain permissible types ofDataObjects are shared.

Currently producers have to publish to multiple sites to reach eachuser. This system allows users not to have to explicitly go to websitessince information flows from producers to consumers (users) in aseamless, passive manner. The system is a universal platform forproducers, consumers, and network supporters to store and share media ofall kinds. The system provides a decentralized architecture within thefabric of the internet itself and connects the resources of each node tocollaborate with each other. Since the system maximizes system resourcesand can assist agents in information sharing, it lessens the burden forproducers to have huge IT infrastructure of servers and bandwidth todistribute their content since it can be shared P2P.

The system allows all nodes to work together in a collective manner,through information sharing. This system cohesion allows the system toconverge toward the best information, intelligence, and DataObjects. Themaster agent supports the positive feedback process between producersand consumers so that information and intelligence can be continuallyconsumed, re-combined, and concatenated increasing the collectiveintelligence of the system.

The system helps to increase global and local intelligence. Agents canbuild their own semantic relations off their separate and uniqueexperiences with information on the internet. Other agents can benefitwhen they share information together. When another agent comes intocontact with the same environmental stimuli or problem space, they donot have to exhaust different pathways to find the optimum solution—theycan use the successful solution of a separate agent that already figuredit out. The process is made easier with a master agent that can be usedas a central collective storage point to pick from several possiblesolutions, choose the best one, and propagate it to other agents in thenetwork. FIG. 15 is a flowchart of an exemplary method 1500 ofcollective data aggregation on a master agent, processing the collectivedata, and redistribution to other agents. Information and intelligencecan be shared on the network in this way.

The system can also find the most “important” information. The CIEdetermines the collective importance of information like DataObjectsbased on how producers and consumers take action on DataObjects throughactions like consumption, production, sharing, rating, etc. Thiscollective importance could be the popularity of the DataObject based onthe number of times it's consumed by users or the importance of theDataObject based on the number of similar news stores produced byproducers about the same event. The CIE can also leverage the SIE toanalyze semantic content and to gauge the impact of the informationbased on what is already in the knowledge base. Based on newinformation's impact on the present knowledge base, furtherunderstanding of its importance can be understood. Through itscollective view of the network and information the master agent has theability to allow the highest quality DataObjects to emerge. It can thensend out the most important information to users that they may not havebeen seen based on individual preferences alone.

The system supports personalized, anonymous targeted advertising forintelligent agents. The master agent can consume advertisements, analyzethem for their semantic content, and output their semantic value toother agents on the network just like any other DataObject. The masteragent includes the semantic value for the advertisement and alsodemographic information to further match users. User agents then insertpersonalized ads into DataObject advertising space. The agents can worktogether so that the advertisement is downloaded from its networklocation, such as an ad website, anonymously.

FIG. 16 is a diagram 1600 of an anonymized DataObject request to awebsite using another agent. Agents can work in a P2P manner or with themaster agent to use other agents in a proxy scheme to anonymizeadvertisement requests from clients. The master agent or user agent cansend requests for download to other helper agents. They would do this inan anonymization network-like manner. In some embodiments, because adsare retrieved in this way, no central points, like websites or masteragents, can collect individual user consumption data, thereby keeping itprivate.

Users within the network producing UGC as well as more traditional mediaoutlets that produce DataObjects can both profit from the individuallytargeted ads. The system allows producers of information to be paidproportionally to the contribution of the information to the system.Therefore the more popular a produced DataObject is the more producerswill get paid based on advertising revenue. The system records this typeof consumption information for the agents and producers (e.g., as shownin FIG. 11). A central point for such a scheme is vital. A P2P schemeamong agents without a central point would be useless to producers. Sucha scheme for this type of anonymized information transfer may have acentralization point for aggregation and verification purposes and alsojudging the overall contribution and popularity to the network. Thisallows the master to accurately pay producers so they have the means tokeep producing information and intelligence. A cycle of overallintellectual growth for the system emerges by making the internet into amore marketable medium for advertisers while maintaining and supportingthe production of new media and information. This allows producers toconcentrate on the quality of their content improving user experienceand the DataObjects they consume.

The system allows user agents to send back advertisement consumptioninformation directly to the master agent. If the user has heightenedprivacy settings even for the master agent, agents can relay consumptioninformation about advertising through other agents. This enables theconsumer to be kept anonymous and data to be kept private (FIG. 1.11).The system is capable of receiving, validating (FIG. 1.12), andcollectively processing these types of anonymized transmissions.

The system allows for safer P2P file sharing. If a producer produces afile DataObject, the master agent will process it for its semanticvalue, make sure it doesn't contain any malware, and get otherinformation about the file like a hash value (e.g., an MD5 sum). Thesystem makes sure no malware is later inserted into the file bymalicious users by relaying the proper MD5 sum in the DataObject essencedata sent to each agent in order for them to evaluate the semantic valueto the user.

The system allows for the sharing of responsibilities between networkagents. Agents can query the master agent for partner agents to helpwith DataObject recommendation computation while an agent is offline.Agents can coordinate the processing of DataObjects so that when a usercomes back online there is no retrieval and processing delay in therecommendation of the newest DataObjects that were produced whileoffline. The system is also conducive toward distributed computing on aglobal scale of all user agents with spare processor cycles at any giventime. The master agent can send commands to the user agents to processand number crunch information it's sent by the master agent. Thislessens the computational power to operate the master agent and itsother backend systems.

Self-Organizing Personalized, Privatized Internet Intelligence Construct

Exemplary embodiments provide intelligent agents executed by computingdevices. An intelligent agent powered by an intelligent constructbecomes a virtual version of a user. Intelligent agents are programsthat carry out a task unsupervised, without explicit commands to do soby applying some degree of intelligence to the task. It should be notedthat an intelligent agent discussed for this system is not an “agent”that a user has to delegate tasks. An agent is not a true intelligentagent if the tasks are pre-programmed by a human and there's no abilityto learn from the environment of information input and ultimatelyincrease cognitive abilities. An intelligent agent has some degree ofcognitive autonomy that is beneficial to overall operation and theuser's experience.

The system uses inheritable, hierarchical memory structures. Animplementation in the real world is Object Oriented Programming whereobjects inherit the characteristics of their parent objects in ahierarchical fashion. Another example is the biological classificationof organisms in biology. Like these systems and examples, the presentaspects of the disclosure not only create meaningful semantic relations,it also creates a logical taxonomy of objects that inherit characters ofparent objects and pass characteristics to child objects. These objectsare usually different semantic types of data like entities, concepts,and context.

Therefore using the this type of storage and retrieval schema as well asthe self-organizing principals of the brain, the construct builds acomplex, ordered structure that can mimic user preferences so that theagent it powers can easily discern which information the user will likeor dislike in future recommendations based on the information, andsemantic relations within that information, that the user consumes. Aself-organizing memory structure therefore programs itself and is uniqueto each user's intelligence construct. Through success and failure(which is accelerated by the collective knowledge of success andfailures from a network of agents that may have already learned from thesame data and situations or problem space variables) a pliable,brain-like storage structure starts to emerge that adapts and changeswith the user over time. This is the quintessential intelligent agentfunctionality. It doesn't need to explicitly programmed or told what todo. For example, it is possible for a user's agent to locate andpurchase concert tickets the user would have wanted because the pricewas sufficiently low. The intelligence constructs internally maps andrelates together multiple information streams in a person's life.

Aspects of the disclosure create an internal semantic structure based onuser consumption and makes decisions and predictions on what futureinformation the user will want to consume. Unlike an unchanging,one-dimensional “recommender” algorithm, this system contains a decisionengine to make predictions. This system learns and gains intelligenceover time. It has short term memory (STM) and long term memory (LTM)components. Similar to how the human brain learns, overwrites, andforgets data over time; this system does the same in order to keep theimportant data while discarding the noisy or less valuable data. Likethe brain's emotions that help guide human decision with the memory ofsuccess or failure for a previous experience, this system keeps tracksof prediction successes and failures for better recommendations in thefuture and also learning which semantic data is important to the user.This construct is not hard coded and can change itself over time. It hasthe ability to make decision and predictions on new types of data andrelate data together continuously in an n-dimensional fashion.

Web-based recommender systems, in particular, have further limitationsdue to the client-server nature of the internet. Because these systemsreside on centralized points like websites, they have inherently limitedcomputational capacities which mean that they cannot performhigh-dimensional calculations over very granular data. Because websitesare stateless and memory-less, they are walled gardens of data. Theycannot understand all user preferences because once a user leaves theirsite they cannot follow them without infringing on user privacy rights.Therefore they have a very limited view of data for the user. To get abetter idea of users, the sites make users explicitly share data aboutthemselves and their consumption habits. This system can follow userseverywhere they go on the internet, determine user preferencesimplicitly, and gain a much better view of user's overall, globalpreferences for information. The system does all this without sharingusers' information or infringing on their privacy rights.

Other recommender system algorithms like collaboration filtering whichtake a more collective view in nature. Their recommendations are basedon the similarity of close neighbor users and also suffer from overgeneralized, high-level data calculations for the same reason listedearlier. This system can make much more detailed analysis of thesimilarity between users and use similarity calculations in a collectivemanner to make better predictions.

Because sites don't have much information about user preferences theytend to show the user massive amounts of information leading the user tohave to sift and filter through mostly “bad” nuggets to find the “good”nuggets of information. Information overload on the internet decreasesthe quality of information a user can consume. It also takes away fromthe user experience.

Historically, only news outlets had access to a news wire. Editorspicked the best stories off the news wire and relayed them to theiraudience. The news wire was the network transport system. The internetitself is the new network transport mechanism that everyone has accessto. This has lowered the barrier to entry and has given rise toblogging, social networks, and other means for individuals to reportnews and spread information and opinions. It's led to informationoverload. Now news outlets or anyone else can pick the best stories foran audience. The intelligence construct helps to solve this problem.Agents are the new, personal-newspaper-editor for each individual. Anagent can personally pick the best stories and information for its user.

To find the best information, users currently have to surf the internet.It's a manual process of discovery and sometimes account set up. Thistakes time, the user has to filter through even more streams ofinformation, and there's no way for most websites to have a goodunderstanding of what information streams each visitor would like best.This is how the system works with the current containerized,website-based internet.

Contrarily, an agent is a bridge to a more seamless integration of anyinformation (DataObject) on the internet. An agent is not just anaggregator. There's intelligence behind all your information streams togive you the best ones. Website aggregators don't do much in the age ofinformation overload except more overloading. An agent is more than asocial media recommender system. It's smart and doesn't just rely onwhat others are doing (like collaboration filtering). It's intelligentand has a memory so it truly understands what a user is interested in ata very precise, granular level. This is accomplished because theconstruct is located on the client's computer and not on a server. It'snot scalable to put such an in-depth calculating device on a third partyserver. For example, a server might be needed for each user. This wouldbe infeasible just from a profitability point of view. With aclient-side agent, the user benefits with less time spent searching,less time looking at things that do not matter to them, and less timetrying to tell the computer what he wants though browser commands.

These constructs can work in a decentralized P2P manner if they arefully autonomous or they can work in a centralized P2P manner with amaster agent that helps out with the semantic and collective dataprocessing and distribution. The communication streams of the agents maybe encrypted for added security over the wire.

The constructs can work together in a collective, swarm-like manner withother agents. They can share information, the distributed processing ofinformation, storage of data, and other logistical networkresponsibilities in cohesion. The system also allows agents to shareintelligence for collective learning which can be efficiently aggregatedand processed on the master agent. Agents can work together in apeer-to-peer (P2P), proxy network to anonymize source calls forinformation; making sure the private data like consumption stays privatefrom centralized points.

Another difficulty transforming the current web into a semantic web isdue to the very nature of the client-server model. It sequesters userinformation via the containerized nature of websites. Because thecurrent internet is stateless, a website doesn't know what a visitordoes once they leave the confines of the site. As a solution, pipes havebeen built between websites so that they can share information on userconsumption habits. The sharing between third parties of user's privateinformation is done to learn more about users' preferences, especiallyfor marketing purposes. Users generally have little control over thirdparty information sharing (e.g., among websites) and are usually unawareof it. Further, some computer systems request account credentials from auser to access contacts and/or other information from a correspondingaccount. From a security point of view, sharing credentials with thirdparties is extremely insecure. Exemplary embodiments operate withoutsharing user behavior information between websites and without requiringa user to share account credentials between websites. For example,exemplary network architectures support the network agents' ability tokeep desired user information private and away from centralized pointslike websites and advertising networks.

By keeping information private, agents can serve up private,personalized, targeted ads to its user without infringing upon userprivacy. This increases revenue in the internet ecosystem of informationproducers allowing more information to be created and disseminated overthe internet.

The system's personalized, privatized advertisements addresses problemswith traditional media and the problems the internet has caused it intrying to adapt their business models to the internet. Traditional mediasuch as newspapers, TV and cable, magazines, etc have historically mademoney from purchase or subscription fees. Also because they were theonly information outlets for people to consume, they could make a largeamount of money off advertisements, even though most of the time theiradvertisements were not applicable to the people seeing them. However,this shot-gun approached to marketing worked because of largeviewership.

The advent of the internet with is free content and stateless natureposed a problem to traditional media and their business models. Therewere no subscription fees and middle-men type network supporter websiteshosted their content. As the supporter site brands become popular withusers, they made the money from advertising revenue off the contentinstead of the producer or copyright holder of the content. Exacerbatingthe problem is the stateless nature of the client-server architecture ofthe internet which makes it hard to effectively and efficiently marketto users without infringing on their privacy rights and/or preferences.

This system provides mechanisms for producers of content like old mediato still profit from their publications. They can profit from both theirnew and old media publications. It allows the efficient marketing tousers without infringing on their rights. Instead of a shot-gunapproach, marketing is more like a sniper rifle catered specifically toa user without infringing on their right to privacy. Since theinformation is relevant and helpful to the user, users may enjoyadvertisements as the system makes it a more seamless experience withother information the user is interested in and consumes. Thus far inthe Web 2.0 world of the client-server model has been “you produce, weprofit”. This system allows the rightful producers of content to profitin a “you produce, you profit” manner. Where before producers werereduced to making “digital pennies” on the internet they can nowlawfully and more effectively market to users to increase profits andmake the internet itself a much more marketable medium.

This system democratizes the flow of information. No source or produceron the network gets preferential treatment or has an unfair advantagebecause of size, leverage, user base, or capital. All producers areequal and have an equal opportunity to produce information, put thatinformation on the network so others can consume it. This is in contrastto the current consolidation of news and media outlets. The capitalistictrend is for the more powerful media outlets to consume smaller, lessfinancially robust outlets. This leads to a narrowing in the availableoutlets and information over time. Instead of the user needing to knowwhere to go to find information, which is usually the sites with thebiggest advertising budgets to reach users through brand recognition,agents pull information which leads to a system that employs a fairermethod of competition for information exchange. Just like websites andmedia outlets, users can produce information and intelligence. Theirconstruct will disseminate any user generated content (UGC) for themthroughout the network. A local classified advertisement of a garagesale has just as much potential visibility as a news story from a majornews outlet because of the ability the construct gives agents to pull,assess information, and present to the user.

Exemplary embodiments provide one or more notable features, as describedbelow.

1. No specific data set is needed to dictate success and failure inorder to train the system as used in conventional Neural Network (NN)program set up. Also the system is not trained in a “brute force” waywhere all possible scenarios of a problem space domain are input to asystem, like a NN, with preconceived output of success or failurewhereby the system gets its internal variable weightings.

2. The system does not need a user to explicitly delegate tasks for itto perform. It knows how and when to act on its own based on data inputand collective agent collaboration. Current P2P and torrent networksjust share information. A user has to explicitly tell the program whatto retrieve and how to share. This system allows for seamless, passivesharing of information where pertinent information desirable to the useris retrieved implicitly on their behalf.

3. The system can recognize and understand changes in user consumptionpreferences over time.

4. The system can make much more granular, deeper level calculationsover higher dimensional problem spaces then mainstream web-basedrecommender systems because of its access to the user and itsnon-centralized, locally based processor.

5. The current internet client-server architecture makes it practicallyimpossible to effectively market to users without infringing on theprivacy rights and/or preferences. This system has the ability topersonalized, yet privatized, marketing efforts so that privacy rightsand/or preferences are not infringed upon while creating sustainablebusiness models for information producers.

6. This system may improve the user internet experience by filtering outbad DataObjects so as to not overload the user with useless (e.g.,irrelevant) information.

7. The current internet client-server infrastructure of websites leadsto isolation of data in “walled gardens.” This architecture is statelessand memory-less as a user navigates between different websites. Becauseof the architectural limitations, engineers have tried to build schemesto share information about users between websites to get a betteroverall view of user consumption. Unfortunately this infringes on userprivacy. This system is stateful and keeps user information private. Thesystem reports anonymized consumption data from users without infringingon their privacy rights and/or preferences.

8. The system supports a master agent in allowing a global collectivememory to emerge based on the experiences of each user agent. Agentssend data to a master agent that is centrally, yet privately, received,process, and aggregated into a global knowledge base.

9. Current advertisements on the internet target users based on thecontent of the webpage the user is consuming or based on the explicitactions users make. Usually these ads are a burden for users to look atbecause they don't apply to the user. This system's ads target usersbased on their interests and demographics. Advertisements are moreseamless and interesting to the user because they apply particularly tothe user. Like the other types of DataObjects a user consumes, usersbenefit more from advertisements that cater to their preferences.

10. Since the system is not stateless, it keeps users secure byostracizing malicious users and rogue agents that publish maliciousDataObjects and who seek to harm others on the network. Agents cancollectively work together to notify each other of malicious entitiesand keep users away from those entities. The system inherently can blockspam and other worthless information as well as act as a social firewallfrom unwanted outside user transmissions.

11. The system enables average and novice internet users to useresources efficiently. The internet becomes less intimidating for newusers with its endless options, having to know about certain sites thatperform certain functions, and its overwhelming information overload.It's a much more passive, seamless experience for the user. The passiveexperience will provide the elusive “veg-factor” of TV on the internetas more people spend time online instead of in front of the TV. The“veg-factor” has been elusive on the internet because sites, such asmainstream video sites, have been unable to provide such an experience.With the intelligent flow of information to the user, a user maypassively consume the information they like most. Anyone has the abilityto produce DOs, get those DOs to interested users passively through thenetwork of agents, and make money off the DOs with personalized,privatized advertisements. The system will automatically pick up newlyproduced information, analyze it, and send it out to the network ofagents where people interested in it will be able to consume it. Thismore passive experience is in contrast to current systems of producersexplicitly having to put information on websites and consumersexplicitly having to go out and find the information to consume it.

12. The decision engine is n-dimensional where new algorithmicdimensions can be bolted on. It has the ability to continually adapt tothe user and change over time. It can also adapt to the granularity ofcalculation possible based on the computational power available.

13. The agent inherently enables filtering out bad information nuggetsso that the user does not experience information overload.

14. With a construct assisting the user's internet consumption, the usermay spend less time searching, less time looking at DataObjects thathave no value to the user, and less time giving the computer explicitcommands on what it should be doing.

15. The construct provides personalized and targeted advertisements touser while keeping their preferences and consumption anonymous. Thisallows producers of information and marketers to benefit withoutinfringing on user's privacy.

16. The system understands similarity. It knows how similar DataObjectsare to each other. Similarity between DataObjects enables the constructto avoid presenting redundant DataObjects to the user. It allows theuser to decide and set the threshold of similarity between DataObjectsfor avoiding redundant recommendations.

17. The construct provides personalized channels or streams ofDataObjects for the user. These channels can be created implicitly bythe construct or explicitly by the user. Popular channels can becollectively shared between constructs that have similar users.

18. The locally installed construct provides scalability advantages overthat of a web-based central point in doing in-depth, granular userrecommendation computations on DataObjects.

19. The system facilitates eliminating unfair advantages to informationproducers that reach audiences simply because of market position andbrand recognition alone. The system brings equality to the disseminationof information and ability to reach users. Information has equal chanceto reach any user because the presentation of the DataObject to the useris based on preferences of the user. Anyone has the ability to produceDOs, get those DOs to interested users passively through the network ofagents, and make money off the DOs with personalized, privatizedadvertisements. The system will automatically pick up newly producedinformation, analyze it, and send it out to the network of agents wherepeople interested in it will be able to consume it. This more passiveexperience is in contrast to current systems of producers explicitlyhaving to put information on websites and consumers explicitly having togo out and find the information to consume it.

20. The intelligence of the system may increase over time based oncollective knowledge shared between other agents and master agents.Agents can learn autonomously and share experiences with each other in apeer to peer manner or with central points, amalgamating experiencestogether to become smarter. Each agent can re-assemble dimensions withnewly added or deleted dimensions and still make decisions, assess howthat affects success or failure, and adapt decision models accordinglyif advantageous.

21. The intelligence and operation of the construct can be integratedand used in a variety of systems. It can provide intelligence fordevices such as an intelligent agent, computer programs, televisions,phones, car, home security systems, and any computational or informationdisplay device. The construct can provide personalized, intelligentoperational features in all these devices. The system can analyze thesemantic value of many different types of information streams based onuser preferences, history, and collective intelligence from otheragents. The system can communicate the output of this analysis to usersand other devices. This is in contrast to current systems that useconventional recommender system algorithms and are programmed to handlea specific type of data in a very limited information problem space.Their systems are inflexible and static where as our system can processa much wider problem space and also learn, adapt, and change over time.The construct is able to sync state of the agent between devices. It canchange the depth of computation and personalize DO types shown perdevice.

22. The system is designed as a generalized intelligence system. Thesystem handles any problem space in which decisions may be made based onsemantic data. The algorithm provided adapts to the problem space by itsown actions and with help from the SIE.

23. Decision models allow construct to make decision and take actions,including making a recommendation, but also potentially includingperforming an action in the real world based on action model.

24. Autonomous agents learn on their own, sharing with each other in apeer to peer manner or with central points, amalgamating experiencestogether to become smarter by new intelligence and learning sharing.Each agent can re-assemble dimensions with new added or deleteddimensions and still make decisions, assess how that affects success orfailure, and adapt decision models accordingly if advantageous.

25. Advertisement personalization may be enhanced for an advertiserand/or producer while keeping such personalization private from them.The system may function as a secure intermediary or “middle man” betweenthe producer, ad network, and the user

26. The system enables passive retrieval of DOs (e.g., advertisements)for the user while keeping user information private from central points.

27. DOs may be passively shared with other users without explicitaction. This is in contrast on how people share information now throughsocial media where they have to take explicit action, like clicking a“share” button on different sites to share content. Agents automaticallyshare consumed DOs (if user permissions allow) with each other andfilter if those shared DOs are shown to a user based on user preference.All sharing and filtering are done under the covers—all done for theuser so he only sees relevant information.

28. The system enables any producer of DOs to distribute those DOs tointerested users (passively), and earn revenue from the DO withpersonalized, privatized ads.

30. The SIE may perform computationally intensive intelligenceprocessing and then send data “essence” (e.g., compressed or condensedsemantic data) to the client to make a decisions (on the clientprocessor). A condensed essence list may be transmitted to a clientprogram, and the client program may perform personalized decision makingto recommend content and/or to take action on content.

31. The state of an agent may be synchronized between devices. The depth(e.g., intensity) of computation, DO type, etc., may be selected perdevice.

32. An SIE or SIE-like device may determine dimensions of a problem tosolve, create a decision model, and send the decision model to a client.The client may also figure out new (or delete, changing weightings)dimensions in a problem space as the context might be different fordifferent users or client computers. In one example, a desktop agent canrequest and get more information like elements within an operatingsystem (OS) file system, how they are related to files, what files areand contain, etc., for the local agent to figure out how that may applyto the particular user.

In exemplary embodiments, the system is an application for computingdevices like a computer, phone, TV, car, home security system, etc. FIG.17 is a block diagram of an information system 1700 including an agentsystem 1705 executed by a computing device (described below withreference to FIG. 37). Agent system 1705 includes a self-organizing userpreference component 1710, a decision engine 1715, and an actions andfeatures component 1720. The decision engine 1715 uses user preferencedata (e.g., provided by the self-organizing user preference component1710) to make predictions. A database 1725 stores data provided by,and/or retrieved by, the self-organizing user preference component 1710,the decision engine 1715, and the actions and features component 1720.

The system may be similar to an intelligent agent. It retrievesinformation for the user. FIGS. 18A and 18B are a flowchart of anexemplary method 1800 of an agent taking in information, ranking DO, andpresenting it to the user. It performs actions on behalf of the user. Itcan do this implicitly without being told or explicitly if told to bythe user. It provides a passive internet experience where information isbrought to the user. It can operate in a decentralized or centralizedmanner with a master agent who performs the global semantic processing,classification, packaging, and distribution. Therefore it could beassisted by a master agent or have semantic intelligence engineincorporated into it to operate in a decentralized manner and completeautonomously.

This construct can support a fully or semi autonomous agent. From alogistical perspective based on network and local resources, it's moreefficient to do the information discovery, semantic value processing,packaging, and distribution with a central point. An autonomous agent orcompletely decentralized network of agents could accomplish the sametasks but it would take massive amounts of computing power on the clientcomputer, a bandwidth-costly information syncing algorithm between allthe agents, and high amounts of bandwidth. Therefore a central point maybe used for such a semantic network so that user agents can spend theirtime evaluating information for the user and learning.

Self-Organizing User Preference Component

The agent's intelligence is self organizing. It will automaticallybecome “smart” around the data put through it. There is no hard codingfor the agent's intelligence. It can handle any type of semanticallyrelated information.

The construct works with a master agent and/or other user agents on thenetwork to send and receive condensed DataObject informational feedsthat have been summarized and packages for quick processing. FIG. 19 isa block diagram of an information exchange system 1900, similar toinformation exchange system 100, described above with reference to FIG.1.

Based on the semantic feeds the agent knows the essence of DataObjects.With user consumption this information acts as the environmental stimulithat the construct uses to build its intelligence structure. It has anintelligence structure that includes semantic relations that are used togauge user preferences so that it can make recommendations on newDataObjects to be presented to the user. It stores semantic data sentfrom a master agent that extracts semantic essence of the data. Thisallows the construct to store and understand user preference forsemantic data like entities, concepts, context, etc. The agent can alsotake in semantic formats like OWL, RDF, etc to understand DataObjectsand incorporate into user preferences.

Based on consumed DataObjects, the system starts by incorporating thelocal semantic data found in those DataObjects into the global semanticrelational data memory system of the construct. SPO, entities, concepts,and contexts (general and categorical) are related together based onlocally mined DataObject composition and already known global relations.These are incorporated into the global user preferences.

Data is examined and globally stored on a more short term basis. If overtime any one of the relations is shown to be more significant, againbased on local relations in DataObjects, it will be graduated to alonger, less constrained, memory. FIGS. 20A and 20B are a flowchart ofan exemplary method 2000 for moving data from short term memory to longterm memory and “forgetting” (e.g., discarding) data. The system triesto quickly weed out non-significant relations as to not overload thelong term relation memory. If the short term memory wasn't trimmedperiodically the system grow too large too quickly and becomeinefficient at retrieving data. It should be noted that data within longterm memory (LTM) is not permanent. It too is deleted from the systemover a much longer time period if relations turn out to be weak.

The content within the DataObjects determines how preferences getorganized. The system incorporates new preferences as appropriate andover time tears them down in STM & LTM if thresholds aren't met.Insignificant preferences are discarded. Multiple preferences areconnected together into a network of connections building orderedsemantic relationships for user preference decisions and predictions onDataObjects. This allows the preferences to change as the user changesso the construct can make good recommendations to the user through time.

Decision Engine

This decision and prediction process is the last step in the intelligentthought process before action is taken. Based on outcomes, emotionalfeelings help humans and other organisms gauge the success or failure ofactions. If the organism felt the outcome was a bad one, it was likely afailure and a situation to avoid in the future. Likewise a situation theorganism feels good about was probably successful and will influence itto repeat the situation again. The system's decision engine works in asimilar fashion. It is a continual feed-back loop of prediction,recommendation, and finally reporting with success and failure whichhelp to reinforce the stored semantic relations within the intelligenceconstruct. FIG. 21 is a flowchart of an exemplary method 2100 of anagent recording consumption information and altering internal relations.

The decision engine is capable of extremely granular, nuancedcalculations in decision making. It is a hybrid system whereself-organizing intelligence works in tune with the decision engine. Thesemantic relational structure's information is queried so a decision canbe made based on current environmental stimuli in the form of DataObjectessence information. The agent predicts if the DataObject should berecommended to the user based on its semantic essence which is packagedup and distributed by the master agent. This allows user agents toquickly process thousands of DataObjects. Calculation granularity islimited by the local computational power and time constraints forDataObject delivery to the user.

To make meaningful predictions the decision engine mines the userpreference data within the intelligence construct. It uses semanticrelations and user preference values to make a prediction. It also takesinto account data such as entities, categories, concepts, etc fromwithin the DataObject's composition that it's sent from the masteragent. Based on previous experience with this data and how the data isrelated, the decision engine can make predictions on the value of theinformation to the user.

The decision engine is not static. It may change over time, just likethe user's preferences. It can adapt its algorithm to the availableresources on the local client. It can adjust the number of relationstaken into account for semantic data calculations. It continually triesto improve itself by finding the most important, and even new,dimensions for the user. New logic can be bolted on to the decisionalgorithms from the master agent and other user agents to further try tooptimize prediction. New senses can be added as well such as vision,location, etc. for the construct to make decisions on.

The construct can adjust the complexity of its algorithms and depth ofits decision to the computational resources available on the clientdevice. It can also use a quicker ranking algorithm to save time todeliver the DataObjects to the user as well. It does this by decreasingthe granularity and number of semantic relations and dimensions takeninto account for each DataObject. This is useful for different mediumsand devices that the construct could reside on. Phones have smallerprocessors than conventional computers so the agent can adapt itsdecision algorithms to the environment automatically.

The decision engine is n-dimensional and can make decisions on new typesof data at will. This new data can be incorporated to the algorithm foruser's global preferences as well as how it relates to other, olderdata. New dimensions can be found, shared, received from other agents.The custom content information stream predictions can be based on allsorts of dimensions like collective purchases, demographics, race, sex,location, employment, etc. The algorithm can change based on context.Different types of information sources and data object types can havedifferent ranking algorithms applied. FIG. 22 is a flowchart of anexemplary method 2200 of ranking data objects based on data object type.

New ones can be added, while old, less important dimensions can be takenout. If deleted dimensions become valuable again later then they can bere-added as the user changes. As an example a remote construct couldfigure out that its user, who watches movies, always wants to see newDVD releases on Tuesdays. It will figure out that day of the week is asignificant decision dimension for some users. This agent can share itwith other agents or the master agent. If applicable, the constructshares these insights with a master agent to incorporate into a globalknowledge base and/or distribute to other agents on the network foralgorithmic trial purposes. Other similar users interested in movies maybenefit from taking into account this new dimension when it comes torecommending DVD movie DataObjects It can also be adopted globally bythe network if the majority of movie watching users find that it addsvalue to DataObject prediction.

New logic for new types of information can be bolted on and incorporatedinto algorithm. The algorithms are just models with separate parts thatcan be put in, taken out, or edited. As the media-scape incorporates newtypes of data, new algorithmic modules can be added to the construct toreplace old outdated ones.

Each agent's repository of semantic relations are different; however,they can work together to find optimum algorithms for their users. Theycontinually optimize themselves based on old training data fromconsumption records. A neural network is used to find the optimumvariable weights for the algorithm for each user. FIG. 23 is anflowchart of an exemplary method 2300 of using a neural network (NN) tooptimize an algorithm with old consumption data. It should be noted thatthis is not the algorithm itself. It's used to further personalize thealgorithm for each user. It discovers the parts that are most and leastimportant in the decision calculation process for the inneroptimizations by weighting most significant variables.

Success and failure is based on the recommendation of a DataObject andwhether or not the user consumes it. The construct deems it a successfuloutcome if it is consumed and a failure if the user views the DataObjectbut doesn't consume it. The construct is locally stored on the user'scomputer so it has more in depth access to user behavior outside ofinternet consumption. It knows what the user does outside of a certainsite or browser. For example, it can see how often a song is played onthe user's media player. The greater the insight into the user'sbehavior, the better recommendations the construct can make for theuser.

The agent is capable of general learning instead of just detailedlearning. Generalized learning allows agent to apply detailed knowledgeto other related and similar knowledge. One application of this would bethe construct navigating a user through an entire knowledge base thatthe user is interested in. As an example it could take the user throughdifferent connected music genres. FIG. 24 is a diagram 2400 of anexemplary hierarchical music genre taxonomy. Once a user exhaustsconsumption in one music genre, the construct can navigate up or downthe music genre taxonomy. It can find sibling, parent, or child genresin the hierarchy. From nearby nodes, the construct can find new musicfrom different yet closely related genres to recommend. Preferences andsemantic relations are stored in a hierarchical memory structure. Itallows the agent to navigate the taxonomy taking the user through allrelated, local knowledge areas. The construct is not limited by new,incoming data. The construct can understand and recommend olderDataObjects as well such as composers of classical music, 70's TV shows,or ancient Greek philosophers. DataObjects and knowledge do notnecessarily have to be time based.

The system has anti-convergent safe guards (e.g., FIG. 21-H). Theconstruct is a feed-back system where preferences build up over time. Ifthey built too quickly before they can decay then the system can becomeimbalanced and converge toward a preference that is too strong andoutweighs other preferences. If the decision engine didn't have such amechanism in place it would soon just recommend and predict DataObjectscontaining the convergent preference thereby degrading the quality ofDataObject picks for the user.

Using the decision engine described above, the construct can makedecisions and predictions. It can deliver custom tailored picks for eachuser through the traversal of the user's semantic data preferences. Thecomposition of the decision engine may be unique to the user. Exemplaryoperation of a decision engine is described below with reference to FIG.36.

System Features

The system has several features that make it more useful to a user. Theconstruct has a cold start program meant to facilitate making goodrecommendations to the user as quickly as possible after installation.FIGS. 25A and 25B are a flowchart of an exemplary method 2500 for coldstarting recommendations. In the beginning the system will have noinformation about the user. There are several ways it can quickly learnabout the user. The system will mine the content of the user's onlineaccounts such as email and social networking sites. It will get all theuser's contacts from those accounts and see if any of them have agentsor publicly accessible information. If they do the system can quicklyfind common preference with the local group of contacts. The user islikely to share these common preferences. The cold start program alsoallows users to explicitly input any preferential data on categories,context, concepts, entities, websites, profession, age, sex, location,etc. Any content that is gleaned from the user's accounts can besemantically processed by a master agent with the data essence sentback. The master agent can also find user similarity right away withcontacts and other users within the network (PSTU). Based on theDataObjects the user's PSTU are consuming, DataObjects can be sent. Inthis way agents can collectively work together to expose preferencesthat contacts and PSTUs have in common.

The agent can also learn about the user through the implicit actions ofthe user. The user does not have to explicitly tell the agent what to door what the user likes. The agent can infer preferences simply from whatuser's activities. For example the agent would be able to see what songsa user plays another application or on other devices. From this it canlearn preferences and know not to recommend recently consumedDataObjects.

These agents are capable of being centralized (e.g., as shown in FIG. 4)and/or decentralized. (e.g., as shown in FIG. 7). Centralized agentscommunicate with a central point of some kind to get DataObject feedupdates and find other agents on the network. Decentralized agents don'treport to a central point. They rely solely on an internal routing tableand autonomously query other agents for DataObject updates and how tolocate other agents on the network. The user has the ability to set theconstruct to a decentralized mode of operation. To receive incomingmessages so that agents can communicate in a decentralized manner theywill set a NAT mapping for the internet gateway device, such as arouter, for the LAN in which the agent is operating. This port forwardsthe router to send incoming messages from external agents to thenetwork's internal agent.

Both centralized and decentralized agents work in an anonymizationnetwork to relay data in a more secure fashion. They can shareDataObjects, including advertisements, in a P2P manner and do itanonymously through the anonymization network. Sometimes agents will notwant to report consumption information for collective learning directlyto the master agent because of privacy concerns. Therefore they can usethe anonymization and allow other, trusted agents to report consumptiondata for them. Agents work together in anonymization so that consumptiondata is not directly reported to central points (e.g., as shown in FIGS.11 and 12). Therefore a central point cannot track the preferences ofeach individual agents and users since calls in are coordinated andanonymized.

Agents can get a feed of DataObjects from a master agent (e.g., as shownin FIG. 2) or other agents. Some of these DataObjects areadvertisements. The agent can pick the ads that a user would be mostinterested in. For example a soccer coach may be interested when teamjerseys go on sale before the start of the soccer season at the localsoccer shop. The agent can pick ads for users that are helpful to theusers. FIG. 26 is a flowchart of an exemplary method 2600 of replacingan ad for a user in a webpage or video. The construct determines thepersonalized, private ads that are displayed to each user. This is asimilar process to the agent picking personalized DataObjects such aswebpages, media, events, etc. Agents work together to deliver newadvertisements and the semantic content of the advertisement. They alsoreport consumption information about both DataObjects and advertisementsin an anonymized manner (e.g., as shown in FIG. 16). As stated earlierto mask user advertisement consumption, agents can relay information toa central point like a master agent through anonymization network ofagent proxies. This facilitates keeping user ad consumption private. Insome embodiments, the reported browsing and consumption data cannot betied back to an individual. This creates a healthier internet ecosystemby increasing revenue for information producers so that creation offuture information is funded. Agents integrate with advertisementnetworks and websites to work in cohesion for displaying advertisements.It can use a network's or site's collection of advertisements from an adrepository to target users in a personalized, yet anonymous, way whilestill allowing proper verification of impressions, click-throughs, andconversion statistics to the central points such as websites andadvertisement networks.

There is no extra advertising. Agents simply replace thenon-personalized ads with the targeted ads in the webpages, videos, andother DataObjects that user consumes. An agent can even personalizecommercials as users watch television or listen to the radio. Instead ofads that don't apply to the user and waste time, pertinent ads thatapply to him are inserted for the user to see. Agents can work in acollective manner with ads just like DataObjects. They can tell otheragents with user that possess similar advertising preferences which adshave worked best for their user. In this way other agents can betterrank ads from not just an individual preference sense but also acollective sense. Agents can integrate with other programs such as othercomputer programs like a computer's browser to help deliver customizedinformation, including ads, to further personalize the user experience.

Agents keep internal routing tables of contact and PSTU networklocations. They can use these agents as an anonymization network toroute messages and other information to a central point. They can alsouse them to request DataObjects from a central point like a masteragent. In this way the central point may have no awareness of who isreally requesting or consuming information keeping everything privateand not allowing users to be tracked. The agents can specify the extentof the routing table and if contacts of contacts (commonly called Friendof a Friend—FOAF) can use them to route messages as well. In this waythey can create routing tables and sync data with each other when IPaddresses change so that the flow of information isn't hindered in adecentralized network.

These agents work collectively in a number of ways. The basicintelligence (brain-like) structure, which is the foundation of theagent, will be used for greater and greater actions over time as theswarm learning allows agents to bolt on new knowledge of other agentsinto their memory structure (e.g., as shown in FIG. 15). With a commonsharing interface between agents these new wirings will continually bebolted on to the agent's memory structure to make it more sophisticatedand useful to its user. New programming code can be sent to the agentfrom a certified master agent, new semantic relations can be sentbetween trusted agent contacts or the master agent, new algorithm partscan be shared between trusted agents or the master agent.

Agents understand similarity in user preferences and share informationbetween each other based on similarity. They can generalize problemsfrom other agents and use this knowledge to solve specific problems fortheir user. Other intelligent agents store information that the userenters like “send flowers to mother on Mother's Day”. As agents worktogether and agent gets to know their user it can recommend, ifapplicable, that the user send flowers for Mother's Day.

Constructs can coordinate and share responsibilities with each other.Agents can autonomously find partner agents or they can query the masteragent for partner agents to help with DataObject computing whileoffline. Agents can coordinate the processing of DataObjects so thatwhen a user comes back online there is no retrieval and processing delayin the recommendation of the newest DataObjects that were produced whileoffline. These agents will trade off processing duties while the otheris offline to increase the user experience for both of their users.

Agents communicate with each other over a secure connection. They canuse a central point like a master agent as a key store for agentverification and to facilitate the public/private key exchange (e.g., asshown in FIGS. 9 and 10). Each transmission is also tokenized so thatagents are not susceptible to replay attacks by malicious users. Allsystem transmissions, including transmissions between the master anduser agents and also transmissions between just user agents areencrypted. The constructs use a public/private key encryption scheme.For agents to be able to communicate securely in a P2P manner, they mayhave some way of verifying that the other agent they are trying tocommunicate with is the actual agent and not an imposter. They can workwith a master agent that acts as the certifying authority for agentidentification. As certifying authority it authenticates the identity ofeach agent before they can communicate over an encrypted stream. Oncethe master agent confirms that both agents really are who they say theyare, it provides the public keys of each agent to the other. This allowseach agent to then decrypt messages from the other agent while makingsure that it is communicating with the correct agent on the other end.If there are agents in the network trying to masquerade as an agentdifferent from themselves, the system can record their maliciousactivities. Agents can learn from the master or each other aboutmalicious network elements and ostracize them from the network.

The constructs has internal security features as well. It can facilitatesecure web account integration by storing random passwords and securityquestions that are independent to the user so that malicious hackerscannot data mine user public data on websites to try a socialmedia-style attack to take over the account. Agents can be back upsecurely, encrypted on an online drive of a network supporter. Only theuser will have the ability to unlock the backup.

User Interaction

The construct can reside on and/or be executed by any computing device(e.g., any device with a processor). It can be put in a phone, TV, car,home security system, etc. It can be used to assist other applicationssuch as being a plug-in for a web browser application. The userinteraction for all devices takes place through a user interface (UI).There are several areas of knowledge and data consumption in the UI.Each major area has sorting, filter, and custom viewing options. Theagent picks the best DataObjects types for the user and displays inthese areas.

The UI contains Queues of DataObjects that have been selected for theuser. Queues are lists of DataObjects for the user to consume or thatalready have been consumed in some way. Queues can be based on bestDataObjects from all channels, saved DataObjects, consumed DataObjects,etc.

The UI contains Channels. These can be made dynamically by the agent,explicitly by the user, or shared collectively between agents forsimilar users. They contain a list of DataObjects for the user toconsume. There are many different types of channels based uponcollective consumption like friends or PSTU, network popularity,top-rated, etc; user preference, semantic data like entities, concepts,and categories; and there can even be custom channels made by the user,agent, or collective by other network agents.

Based on possible SPO's and semantic relations users can custom makechannels tailored to their specifications. Based on the channelcomposition, the agent will retrieve DataObjects that conform to thecustom criteria. Channels can be general or specific. They can be asgeneral as “Tell me when congress passes a new law” or “Retrieve newepisodes of Family Guy when they come out”. A user can construct verygranular channels well such as “Retrieve all video highlights of LionelMessi scoring goals for Barcelona in La Liga” will be sent to the user.Agents can try to autonomously make these channels based on userpreferences as well. The constructs can see if the user is receptive tothe DataObjects it produces. If the user consumes those DataObjects, itwill ask the user if the channel should be adopted. Adopted channels canbe shared collectively with other agents since similar users may beinterested in the same custom DataObject feed. Channels can be based ondifferent channel types such as popularity, user preference, importance,etc. Channels can also be based off networks that the user is a part ofor similar people that the user is connected to. These networks can becreated to be public or private among users and agents to shareinformation about different knowledge areas or simply for datasynchronization. Channels can also present the user with specificDataObject types such as videos, people, news stories, events, etc.

Channels can be set up to integrate with online accounts as well. Forexample the agent could integrate with a user's online bank account andgive various updates such as the account balance each day or wheneverthe account balance is below a certain amount. These account updates areconfigurable by both the agent and the user.

The UI contains a messaging section for user to create messages andconsume messages from various online accounts like email or socialnetworking. Users can also send messages directly to users of otheragents strictly through the network. The UI contains all the users'contacts from the various accounts and the network agents. Based onACL's the agent automatically disseminates user consumption data toother trusted agents so their users can benefit from each other throughconsumption in similar, preferred knowledge areas.

The UI contains mechanisms for displaying data such as a browser fordisplaying web pages as well as and media player for playing videos, TVshows, and movies. The user has the ability to consume DataObjectsoutside the construct in different browsers or media players as well.

The UI contains calendars for upcoming events like concerts, future TVshows or movies, or a dentist appointment. The user can tell theconstruct which DataObjects to retrieve once the DataObjects arereleased. For instance, the user could tell the construct to retrieve afuture episode of Family Guy when it comes out later in the week.DataObjects can be synced to other construct instances on other devicesthrough the calendar.

Agent behaviors can be set up by user or learned dynamically by theagent. They can be shared collectively among agents and also through amaster agent for similar users. The agent behaviors can sync databetween different agent instances that reside on different devices. Thedata syncs are intelligent so the agents can synchronize data anddevices based on user habits. They can auto download content based onthese habits and individual device limitations. For instance if abusinessman rides the train to work each weekday, the agent can syncstock markets news, videos, and also textual stories to the user'sportable computing device. It the device's bandwidth is limited theagent can figure this out based on data type consumption and send lowbandwidth DataObjects that are easily and quickly downloaded such asnews stories that are in textual content form. In this way the agentintelligently pulls in information for the user ahead of time to improvethe user experience by saving time and resources.

Agent behaviors can be made explicitly by the user. However, likechannels, constructs can learn successful behaviors from other agents ina collective manner. If the users are similar, it can learn fromconnected agents of similar users that were successful in presenting newchannels to the user. Behaviors include the relational knowledge betweena behavior and a network supporter so that the syncs can be integratedand automatically sync to online drives. Videos can be sent to the workcomputer with high bandwidth. The agent can learn over time based onconsumption and generally from the collective to send certain DataObjecttypes based on capacity of the device to receive and store data. Forinstance, it won't send big files and send the most importantDataObjects to a portable device.

These behaviors also include action behaviors for the agent to performspecifically with certain DataObjects. The behaviors are combined withsemantic relational data so it knows what a website is, and that awebsite holds data objects at a certain location such as a URL. With thecorrect logic modules, an agent can parse the page correctly anddownload the data for user or replace a certain parts of the page withpersonalized ads or even personalized content. This logic is extractionlogic for the internet (how to get DOs) and also behaviors, newchannels, new additions or updates found for ranking and the decisionalgorithm.

Monitoring

Exemplary methods of monitoring user actions are described below.

The system has a HTTP Proxy that monitors all web traffic that goes inand out of device on which the construct operates. There is two waycommunications between the HTTP Proxy and the agent over a local socketand port. The HTTP Proxy simply updates the agent with new HTTP streams.The agent takes action analyzing the streams and with proper userpermission can anonymously send stream and consumption data to theMaster Agent for further collective processing. The MA may classify thesite or webpage DO and inform the sending agent, possibly through proxyagents, of the semantics of the site, page, video, etc. The user agentwill then update user preferences accordingly for the consumed DO. Theconstruct can also send back modifications on the HTTP stream to theHTTP Proxy. For instance it can send personalized ads for a web pagethat the HTTP Proxy can insert before sending back to the browserwhether a browser plug-in exists or not.

It can determine the web accounts the user has, account credentials, inaccordance with a browser plug-in or key logger to help with encryptedstreams; account contact interaction; actions taken while web surfing,for instance it can discover the user does bill pay through a bankingwebsite once per month and checks account balances every Friday; andoverall web consumption that occurs outside of the agent. These are alldone so that the construct has a better idea of all user preferences.The HTTP Proxy is also used collectively for website discovery. Forinstance new websites and web-based DO producers, such as a new blogger,can be discovered. If enough users go to a new site the Master Agent maydecide to incorporate the site or blog into the system as a producer andanalyze its DOs to send back out to other network agents.

Through repeated actions like the user going to a banking website tocheck the account balance every Friday, new decision model dimensionsand user actions can be discovered. Relevant data can be extracted andautomatically delivered to the user so he doesn't have to take explicitactions anymore. As mentioned above, the agent, with the help of MasterAgent which harnesses data collectively from the network of agents andsimilar users, can discover new dimensions for global decision models orlocal models used by the user. In this case it may discover the website,action, and day dimensions to learn to make decisions and DOrecommendation based on them. It may learn to tell the user his bankaccount balance on Fridays for example.

The construct can interact with program plugs-ins such as an agentbrowser plug-ins. Agent browser plug-ins allow the user to do most ofthe functions and features of the agent within the browser such asconsuming new DOs, communicating with contacts, watching videos (webvideos, TV shows, movies, web cams, etc), and getting updates on new DOsand contact status changes while the user surfs the web. The agentbrowser plug-in harnesses API's that the browser exposes. The agentplug-in acts as another UI container for the agent to populate withinformation. The agent still does its own DO processing in thebackground. The agent updates the plug-in with new data while theplug-in updates the agent with user actions. The plug-in can modifyunderlying web page HTML as well. The agent can send personalized ads tothe plug-in so it can replace the current ads with the personalized onesfor web pages in the browser.

The construct exposes APIs that allow other programs to communicate withthe construct. Other local programs can communicate with the agent on alocal socket and port on the computing device. Other programs caninteract with the agent to get personalized ads, DOs, contact statusupdates. The construct can do intelligence calculations for the programsas well. The user would be explicitly prompted to allow certain actionsand information sharing to take place between the programs like offloading intelligence calculations or serving up personalized ads.

Through the HTTP Proxy or an agent browser plug-in, the construct caninteract with websites to delivery custom content for a user. Oneexample would be changing what is displayed on a website such asstories, social media messages, videos, even ascetics of the site likecolor. The construct can publically expose an API so that when websitessend content to a user's browser, they do it in a format compliant withthe API so that the construct or plug-in can quickly filter content togive the user a personalized view of the site's data. This could alsohelp websites that recommend items to users. It could assist thesessites in making better recommendations. This is done in a secure mannerso that user preferences are not revealed to the site unless users takefurther actions on site content. Users have the ability to allow or denythe construct to personalize web content.

The construct could interact with the underlying Operating System (OS)as well to change OS settings if an OS API exists and is exposed tolocal programs. For instance it may tell the OS where to store adocument based on its semantics such as a Word document. The agent wouldanalyze its semantics and tell the user where to save it. Normally theword processing programs like Word will save a new document in theuser's “My Documents” folder. The agent can observe how the user savesdocuments over time. Based on those dimensions, it can figure out thatthe user usually doesn't save Word documents in the “My Documents”folder as much as saving in user created directories near other relevantdata to the Word document. The agent could tell the OS not to use the“My Documents” by default and instead specify which directory to use.This is how the agent can locally or via Master Agent, whichcollectively monitors new and successful actions other agents take withtheir users, pick up dimensions and incorporate them into the decisionprocess. For the directory dimension of the action, the default “MyDocuments” directory is a weaker factor in deciding which directory tosave data compared to directories with data files with knowledge areasimilarity in user created directories. It should be noted that this isa very simplistic explanation of learning that would be iterative andwould takes a significant amount of dimensions, relations,generalization, and especially processor cycles.

Exemplary Decision Engine

FIGS. 27A, 27B, and 27C are a flowchart describing an exemplary method2700 of operating a decision engine. In exemplary embodiments, aSemantic Intelligence Engine (SIE) remotely builds global semanticintelligence in a self-organizing manner outside of the intelligenceconstruct. The SIE updates network constructs with decision models. TheDecision Engine (DE) within the intelligence construct makes decisionsbased on Data Objects (DOs) semantic essence data sent to the constructvia the SIE and user actions over time (recorded locally by theconstruct). The two parts of the SIE and DE work together. The DEleverages user data that is locally created (data is also forgotten)over time to continually improve on the decisions the agent makes.

1. Dimensions

The dimensions in each calculation represent different variables or datatypes in the problem space used to make a decision. Dimensions are usedin different ways in the decision. Based on the calculation part theyare processed in a certain order; they are sometimes combined with otherdimensions; they sometimes have inner data dimensions to be processed;and they all have different weightings in the decision.

Inner Dimensional Data and Relations—Some dimensions will have innerdimensions to be taken into account. Usually this occurs when there aremultiple data instances of a certain dimension in the problem space ofthe decision. For example a DO may have many entities. Although “entity”may be a dimension, it may be appropriate to calculate how each of theinner dimensions (each entity) contribute to the entity dimension andthe DO as a whole based on its relevance in the DO, its weighting, andits relations to other entities in the DO.

Abstract vs. Value for Inner Dimensions—An example of value data for theentity dimension would be “Brett Favre”. His entity type may be“Football Player”. Brett Favre acts as the value data piece whileFootball Player acts as the abstract data piece. Abstract data isimportant for data generalization purposes as well as for small datasets where huge numbers of combinations exist for value relations in acertain DO type like movies. The value relations may not be asrepeatable over time; however, the general relations will be.Generalized relations, based on abstract data, can gauge user preferencebetter then a weak scoring value relation unlikely to be seen again.Generalized relations will be more repeatable over time and thereforehave a better chance to contribute to overall user preference.

Outer Dimensional Relations—Outer dimensions are relations betweendifferent dimensional data types. User may like to consume videos ofYankee games but not like to read stories about them, a user may likeNFL articles from ESPN but no SI, or user may like to consume articlesabout Albert Pujols when he plays for the Cardinals but not like DOsabout his local charity work. Therefore the decision can be made basedon different outer dimensional relations that are significant in userconsumption.

2. Decision Models

The Semantic Intelligence Engine (SIE) remotely builds global semanticintelligence outside of the intelligence construct. Through the MasterAgent the SIE updates network constructs with decision models. TheDecision Engine (DE) within the intelligence construct uses decisionmodels to make decisions based on DataObjects' semantic essence datasent to the construct via the Master Agent and user actions over time(recorded locally by the construct). The two parts of the SIE and DEwork together. The DE leverages user data that is locally built up (datais also forgotten) over time to continually improve on the decisions theagent makes. It should be noted that terms “Decision Models” and “RankModules” are used interchangeable in the patent when in reference tomaking decisions that score or rank DOs in order to recommend them to auser.

There is a sequence of steps based on the decision model with differentdata types, components, and factors used by the DE to make decisions.Below are explanations of the elements used in making a decision:

Dimensions—The dimensions in each calculation represent differentvariables or data types in the problem space for making a decision.Dimensions are used in different ways in the decision. Based on thecalculation part they are processed in a certain order; they aresometimes combined with other dimensions; they sometimes have inner datadimensions that should be processed; and they all have differentweightings in the decision.

Inner Dimensional Data and Relations—Some dimensions will have innerdimensions that should be taken into account. Usually this occurs whenthere are multiple data instances of a certain dimension in the problemspace of the decision. For example a DO may have many entities. Although“entity” may be a dimension, the system should calculate how each of theinner dimensions (each entity) contribute to the entity dimension andthe DO as a whole based on its relevance in the DO, its weighting, andits relations in the DO.

Abstract and Value Data Types for Inner Dimensions—An example of valuedata for the entity dimension would be “Brett Favre”. His entity typemay be “Football Player”. Brett Favre acts as the value data piece whileFootball Player acts as the abstract data piece.

Outer Dimensional Relations—Outer dimensions are relations betweendifferent dimensional data types. User may like to consume videos ofYankee games but not like to read stories about them, a user may likeNFL articles from ESPN but not SI. The user may like to consume articlesabout Albert Pujols when he plays for the Cardinals but not like DOsabout his local charity work. Therefore the decision can be made basedon different outer dimensional relations that are significant in userconsumption.

Decision Models—The SIE sends decision models to agents to assist themon making decisions. These are sent in such a format so that for anytype of decision the agent has to make, the proper dimensional datatypes, weightings for those dimensions, and sequence of processing isavailable to plug into the decision. All decisions are made based onwhat the SIE determines is relevant for a given contextual problem spaceand has an action associated with it such as making a recommendation.The SIE develops models for these based on experiences it has with datait encounters. The internet and network of agents act as the domain ofits experience; however, with more data, like sensor data, this could beextended into the physical world as well. These models or experiencescan be shared with local user agents. The network of agents can alsofigure out new significant dimensions based on their own experiences andnumber crunching (See example in Other Program & OS Interactions & API'sbelow in the Monitoring section). These local experiences can be sent tothe SIE to do collective computations and processing. If relevant to thenetwork as a whole any local experiences and learning will be globalizedon the network and sent out to other agents to assist in their decisionprocesses. The domain of semantic data is covered specifically but thisprocess and the decision models can be used to make decisions over anyproblem space, even for problems in the real world.

EXAMPLE 1

Golf Putt (physical world)—A human can determine significant dimensionswhen making a putt. From repeated experiences a human knows it's largelybased on slope of green, wind, speed of green and which way the grass isfacing, how far away the hole is versus how far back a person needs totake the backswing for the putter, ball and putter face resilience, etc.There are possibly thousands more variables and dimensions to consideron varying levels. Most levels are very small in magnitude in relationto a few larger dimensions. All relevant dimensions are put together tomake a decision about putting. A human inherently determines how thesedimensions relate together. Then the overall importance of thedimensions themselves and also how they relate to each other is takeninto account when making a decision. The SIE does the same thing so thatuser agents have a way to make decisions on with the proper data,dimensions, relations, and weightings.

EXAMPLE 2

Sports Article Recommendation (electronic environment)—Dimensions forthis decision might include producer of article (website), author,concepts in article, entities, SPO's, and other data and relationships.These are taken into account and applied to user preferences to decidewhether or not to show the DO to the user.

Calculation Parts—Calculation Parts are cascading logic models connectedtogether like links in a chain for decision processing. Many links canbe cascaded together to make a decision as there may be different levelsto the decision based on data types, dimensions, and generalization. Thedecision model determines the order that the cascading calculation partsare processed in. The calculation part determines the order thatdifferent dimensions are processed in. A score is determined for eachcalculation part. The sums of previous parts are weighted against thenewly calculated part. Parts can be for one significant dimension or formultiple less important dimensions. For each part the DE takes intoaccount many pieces of data within a dimension, amalgamates them invarious ways based on weighting, context, and comes up with a score foreach piece of data within the different knowledge areas of thecalculation part. These knowledge area scores are again amalgamatedbased on context, level in cascading chain, etc to come up with scorefor the calculation part. This process can be repeated many times tillthe final DO score is completed depending on number of calculationparts.

Context—The SIE sends a different decision model for each context.Context is like the environmental dimension of the decision. It's anover-arching part of the decision that has the power to change thedecision model itself. It can change dimensions used, weighting, etc.Context is the view or perspective (like a “channel” from UserInteraction section below) in which the decision takes place. Theconstruct may want to make a decision on DOs from a “new interest”perspective. This would dramatically shift weighting between STM andLTM. The STM component would have a much more dominant weighting overthe dimensional data that make up the decision then the LTM component.Data Object type, such as videos, articles, TV shows, messages, etc,also act as a contextual component of the decision. For instance sharedDOs and social media messages are sent from a contact or PSTU. Thecontact or PSTU dimension is brought in for this DO type decision or ina social oriented perspective. Contact and PSTU data may not apply toother DO types or contexts. A popularity perspective may bring acollective social dimension based on the larger viewpoint of the wholenetwork and what it thinks about a DO. New dimensions can be added,deleted, or dimensional weightings can all change based on the decisionmodel for a given context in a particular problem space.

Data Rank Types: Relations and Global Personal Preferences—GPP has norelational bias taken into account. Its value is solely a representationof a user's preference for a particular piece of data like an entity.Relational preferences are based on the relationship and co-existence oftwo or more pieces of data in DOs. Both of these types of datavaluations should be taken into account in the decision process. Eachpiece of data, weather GPP or relational, has a particular weighting ina calculation part. This weighting is combined with its contributionwithin the DO to get its score within the dimension or calculation partfor its particular data rank type (GPP or relational). All GPP datapieces are combined and all relational data is also combined and addedto the overall calculation part score. It should be noted that any pieceof semantic data within the DO, for example a particular entity, canreside in both GPP and relational calculations. The DE would rate howmuch the user likes Brett Favre from GPP and inner-dimensionalrelational perspective. A user may very like Brett Favre but only DOsabout him with Packers and not the Vikings or only prefer DOs about himin a sports context and not a gossip context. The calculation part wouldspecify what type of inner and outer dimensional relations to processand the weightings for each in the DO for a particular context.

Generalized Data—Data can be generalized when a decision is unclear, thesemantic data make up of a DO is uncertain, or the known data is verysparse. Abstract data, relations, and taxonomy parental nodes can all bebrought into the decision process to generalize the data that is knownto get a better understanding of DO make up and how much the user willbe interested in the DO. Generalized Data's weighting is much less thanknown data weightings and may not necessarily be used in the decisionprocess at all if there is enough known data to make a decision on.

Short Term Memory (STM) and Long Term Memory (LTM)—Data progression andforgetting data are used for the intelligence construct to adapt to userchanges in preference over time. Preference data is migrated from shortterm memory to long term memory during its life cycle. At the end of itslife cycle the data is discarded or forgotten by the construct. Dataexists in short term memory for a certain interval before it's migratedto long term memory. It incrementally progresses through its life cyclefrom STM to LTM to being forgotten. Based on what stage the data is init has a certain weighting within calculation parts. For example in anew interest context where DOs are displayed based on user's newinterests, STM has a much higher weighting than it does in a overalluser preference context which values more established preferences (LTM).These weightings are always taken into account in data calculations.Weighting values can vary for different dimensions (entity, concept,general knowledge area, producer, etc) and data rank types (GPP orrelational).

The rate at which data progresses through the memory intervals and iseventually discarded and forgotten is dependent upon data type andfrequency of datum exposure. For example a person entity like “BarackObama” may be forgotten a lot faster than a seasonal TV show like “TheSopranos”. The person entity is continually exposed in the news dayafter day where a user has the opportunity to consume it. The TV showmay only have several new TV show DOs created every couple years. Theyprogress and are forgotten at different rates else a TV show will beforgotten too soon or a person entity may persist too long in userpreference. Combined with user actions this data migration andprogression largely dictates how the DE algorithms change and optimizethemselves over time

Normalization factors—Normalization is used to equalize differentdimensions (inner and outer parts of the same dimension as well) anddata rank types scores within different steps of each calculation part.This helps ensure that no one particular dimension or data tank typedominates the overall score of the calculation part thereby making otherdata within the calculation part insignificant to the score and overalldecision. Normalization is applied to different scoring steps in thecalculation parts based on the difference in data scores that isrecorded over time. A normalization score can be produced for differentdata sets so that they all contribute on equal footing. As the scoringcontribution for a particular data type rises or falls over time thenormalization factors will adjust as well. Factors are helpful becauselarge data (like entities) sets may have lower score contributions foran individual datum versus other data types with small data sets (likeproducers) which will tend to have higher score contributionsindividually. Also for each DO there can be multiple entities while onlyone producer. For GPP and relational scores the producer user preferenceand DO contribution will dominate and render the entity scores andentity relations meaningless to the overall decision. Likewise relationsare spread over a much larger domain of possibility so they will have asmaller overall individual score than individual GPP scores. Both thesedata examples would perform normalization so scoring for each type ofdata or dimension is accurate and has a chance to equally contribute asit should to the decision dictated by the calculation part they residein. Decision models from SIE can tell the construct how to create thenormalization factoring between the data rank types and dimensions for agiven context. The actual normalization score value is user dependentand determine by the local construct.

Weightings—Weightings exist for the different parts of making a decisionbetween dimensions, GPP and relations, abstract and value data, innerand outer dimension, STM and LTM, and even between different calculationparts within the same decision model for a given context. Theseweightings affect the overall contribution of the particular data partfor the decision. For a certain context GPP may contribute 90% of thescore versus 10% for relational data. Within a “new interests” contextSTM will be weighted much higher than LTM.

DO Make Up and Data Part Contribution—Thousands of different pieces ofsemantic intelligence data make up a DO and each one has a contributionwithin the DO. The data includes different data dimensions such asentities, Subject-Predicate-Objects (SPO), concepts, categories, etc.They all contribute to the overall make up of a DO and how the DE scoresit. If a DO has an entity that contributes highly to a DO and the userhas a high preference for that entity the DO will be scored higher inthat portion of the decision than another DO with same entity thatdoesn't contribute as much to the DO make up. Likewise if an entity haslow contribution to a DO and the user only mildly likes the entity thatDO will score even lower in that portion of the decision. Each portionfor dimensions, data rank types, and calculation parts are added andweighted properly to make an overall decision.

Algorithmic Changes and Optimizations—The construct continually finetunes decision model calculation part weightings, data rank types, anddimensions. A Neural Network (NN) based algorithm is used to go over allparts of each decision and find which parts ultimately contribute mostand least to successful user outcomes like DO consumption forrecommendation actions. This employs a brute force method of trying outnew weightings, data rank types, and dimensional combinations over timeto find an optimum decision path. The NN goes back through DO's,rescores them, taking into account which ones were consumed and notconsumed, with different combinations of dimensions, weightings, dataparts used with different calculation parts to optimize and personalizethe algorithm for the user so that the best dimensions, data parts, andthe most accurate weights are used. This not only personalizes the DEfor the user but also optimizes the algorithm so that parts of thealgorithm (dimensions, data parts, calculation parts, etc) that aren'thighly contributive to successful user outcomes like DO consumption arenot used thereby saving time and process cycles to delivery better datafaster to the user more efficiently. The NN is also used to optimize thedata progression process from STM to LTM to finally data deletion.Migrations intervals and decision contribution weighting are optimized.

Adding New Calculation Parts and Learning New Dimensions—New contexts,dimensions, data rank types, calculation parts all can be added andincorporated at any time from Master Agent or local agent. The agent canfigure out new decision model dimensions and data and update existingmodels. The agent will incorporate them into decisions process to see ifthere's any benefit to DO consumption or other successful actions.Successes are kept in the local algorithm and also shared with MasterAgent so other network agents can benefit from the new knowledge to makebetter decisions for their users. (See example in Other Program & OSInteractions & API's below in the Monitoring section) The discovery ofnew dimension is a brute force discovery method traversing all relevant,exposed data in a problem space. The discovery of new dimension isreally just limited to available processing power and spare processorcycles.

Algorithmic Device Depth—Depth of number crunching, number of dimensionsper calculation part, number of data parts per inner dimension can bereduced or “shaved” based on attributes (e.g., processing power, memory,and/or battery capacity) the device making the decision. A mobile phonemay have an algorithm that shaves less likely data (usually lowercontribution and lower user preference scored data/dimensions) so thatthe decision process is streamlined and doesn't use as much processorpower to conserve battery life. Decisions can be based on higher orlower level data as dictated based on time and computation environmentalconstraints. The construct can take the device itself on as a dimensionto learn what DO types a user is likely to consume per device with thehelp of other agents, the SIE, or through the use of the NN optimizationalgorithm. It can easily learn to recommend TV shows and movies for atelevision agent.

Similarity—Similarity calculations can be taken into account for certaincontext as dictated by calculation parts. For instance constructs maywant to determine how similar user preference is to a contact in acertain knowledge area such as music genres when a contact shares a newsong on a social media website. The construct can decide to recommendthe social media message-based song to the user or not partly based onsimilarity of the two users over the semantic domain of the DO as wellas individual user preferences. The calculation parts can tell the DEwhen to use a similarity calculation to help score the decision.

Decision Threshold Scoring—Each decision has a threshold to take actionor not. If the intelligence construct tabulates a score that exceeds thedecision threshold for a certain contextual decision model then theaction is taken such as making a recommendation.

Methods and operations described herein may be performed by one or morecomputing devices. FIG. 28 is a block diagram of an exemplary computingdevice 2800. Computing device 2800 includes a processor 2802 forexecuting instructions. In some embodiments, executable instructions arestored in a memory 2804. Memory 2804 is any device allowing information,such as executable instructions, bodies of text, semantic data,preference data, configuration options (e.g., predetermined durationsfor receiving transmissions), and/or other data, to be stored andretrieved.

Computing device 2800 also includes at least one presentation device2806 for presenting information to a user 2808. Presentation device 2806is any component capable of conveying information to user 2808.Presentation device 2806 may include, without limitation, a displaydevice (e.g., a liquid crystal display (LCD), organic light emittingdiode (OLED) display, or “electronic ink” display) and/or an audiooutput device (e.g., a speaker or headphones). In some embodiments,presentation device 2806 includes an output adapter, such as a videoadapter and/or an audio adapter. An output adapter is operativelycoupled to processor 2802 and configured to be operatively coupled to anoutput device, such as a display device or an audio output device.

In some embodiments, computing device 2800 includes a user input device2810 for receiving input from user 2808. User input device 2810 mayinclude, for example, a keyboard, a pointing device, a mouse, a stylus,a touch sensitive panel (e.g., a touch pad or a touch screen), agyroscope, an accelerometer, a position detector, and/or an audio inputdevice. A single component, such as a touch screen, may function as bothan output device of presentation device 2806 and user input device 2810.

Computing device 2800 also includes a communication interface 2812,which enables computing device 2800 to communicate with a remote device(e.g., another computing device 2800) via a communication medium, suchas a wired or wireless network. For example, computing device 2800 maytransmit and/or receive messages (e.g., requests and/or responsesrelated to semantic data and/or preferences) via communication interface2812. User input device 2810 and/or communication interface 2812 may bereferred to as an input interface 2814.

In some embodiments, memory 2804 stores computer-executable instructionsfor performing one or more of the operations described herein.

Exemplary Operating Environment

Methods described herein may be performed by a computer or computingdevice. A computer or computing device may include one or moreprocessors or processing units, system memory, and some form of computerreadable media. Exemplary computer readable media include flash memorydrives, digital versatile discs (DVDs), compact discs (CDs), floppydisks, and tape cassettes. By way of example and not limitation,computer readable media comprise computer storage media andcommunication media. Computer storage media store information such ascomputer readable instructions, data structures, program modules, orother data. Communication media typically embody computer readableinstructions, data structures, program modules, or other data in amodulated data signal such as a carrier wave or other transportmechanism and include any information delivery media. Combinations ofany of the above are also included within the scope of computer readablemedia.

Although described in connection with an exemplary computing systemenvironment, embodiments of the invention are operational with numerousother general purpose or special purpose computing system environmentsor configurations. Examples of well known computing systems,environments, and/or configurations that may be suitable for use withaspects of the invention include, but are not limited to, mobilecomputing devices, personal computers, server computers, hand-held orlaptop devices, multiprocessor systems, gaming consoles,microprocessor-based systems, set top boxes, programmable consumerelectronics, mobile telephones, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Embodiments of the invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. The computer-executableinstructions may be organized into one or more computer-executablecomponents or modules. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks or implement particular abstract datatypes. Aspects of the invention may be implemented with any number andorganization of such components or modules. For example, aspects of theinvention are not limited to the specific computer-executableinstructions or the specific components or modules illustrated in thefigures and described herein. Other embodiments of the invention mayinclude different computer-executable instructions or components havingmore or less functionality than illustrated and described herein.

Aspects of the invention transform a general-purpose computer into aspecial-purpose computing device when configured to execute theinstructions described herein.

The embodiments illustrated and described herein as well as embodimentsnot specifically described herein but within the scope of aspects of theinvention constitute exemplary means for determining, distributing, andacting upon semantic data.

The order of execution or performance of the operations in embodimentsof the invention illustrated and described herein is not essential,unless otherwise specified. That is, the operations may be performed inany order, unless otherwise specified, and embodiments of the inventionmay include additional or fewer operations than those disclosed herein.For example, it is contemplated that executing or performing aparticular operation before, contemporaneously with, or after anotheroperation is within the scope of aspects of the invention.

When introducing elements of aspects of the invention or the embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

Having described aspects of the invention in detail, it will be apparentthat modifications and variations are possible without departing fromthe scope of aspects of the invention as defined in the appended claims.As various changes could be made in the above constructions, products,and methods without departing from the scope of aspects of theinvention, it is intended that all matter contained in the abovedescription and shown in the accompanying drawings shall be interpretedas illustrative and not in a limiting sense.

What is claimed is:
 1. A system comprising a master computing devicecomprising a processor communicatively coupled to a memory device, saidmaster computing device configured to: retrieve a plurality of dataobjects (DOs) from one or more remote user computing devices, whereineach DO is associated with a network location from which the DO isaccessible; determine semantic data associated with each DO of theplurality of DOs, wherein the semantic data describes content of theassociated DO; receive, from a first user computing device of aplurality of user computing devices, a request for DO information; inresponse to the request, provide the requested DO information includingthe locations and the semantic data associated with the retrieved DOs tothe first user computing device by one or more of the following: (a)transmitting the locations and the semantic data to the first usercomputing device, and (b) instructing the first user computing device torequest the DO information from a second user computing device to whichthe locations and the semantic data were transmitted before receivingthe request from the first user computing device; maintain a relay countassociated with each of the plurality of user computing devices, whereinthe relay count indicates a quantity of times the associated usercomputing device has transmitted DO information to another usercomputing device; identify a set of non-contributing user computingdevices associated with a relay count indicating the associated usercomputing device has not transmitted DO information when requested totransmit DO information; and disregard requests for DO information fromthe non-contributing user computing devices.
 2. The system of claim 1,wherein the master computing device is further configured to: record atransmission of the locations and the semantic data associated with theDOs to the second user computing device; and select the second usercomputing device based on the recorded transmission.
 3. The system ofclaim 2, wherein the second user computing device is associated with arelay count, and the master computing device is further configured to:select the second user computing device based further on the relay countof the second user computing device being less than a predeterminedrelay quota; and increase the relay count associated with the seconduser computing device based on receiving a notification from the firstuser computing device that the second user computing device hastransmitted the locations and the semantic information of the pluralityof DOs to the first user computing device.
 4. The system of claim 1,wherein the master computing device is further configured to: calculatean expected hash value associated with the DO information; and transmita warning associated with the second user computing device when thesecond user computing device transmits DO information that does notvalidate against the expected hash value.
 5. The system of claim 1,wherein the master computing device is configured to transmit therequested DO information by transmitting locations and semantic dataassociated with a plurality of advertisements.
 6. The system of claim 1,wherein the master computing device is configured to determine thesemantic data associated with each DO of the retrieved DOs by extractingthe semantic data from the DO.
 7. The system of claim 1, wherein themaster computing device is configured to determine the semantic dataassociated with each DO of the retrieved DOs by receiving the semanticdata from another computing device.